کاربرد هوش مصنوعی در هنر موسیقی: بررسی نظاممند
محورهای موضوعی : هوش مصنوعی و رباتیک
1 - دانشگاه پیام نور، تهران، ایران
کلید واژه: هوش مصنوعی, موسیقی, تولید موسیقی, یادگیری ژرف,
چکیده مقاله :
فناوری هوش مصنوعی علیرغم عمر کوتاهش که از حدود سال 1960 توسط جان مک کارتی آغاز شد، تقریبا به تمامی عرصهها ورود جدی داشته است و آن را تحت تاثیر شگفتیهای خود قرار داده است، ازجمله هنر موسیقی. پژوهش حاضر با هدف شناخت و معرفی کاربردهای هوش مصنوعی در موسیقی، به بررسی نظاممند ادبیات موضوع هوش مصنوعی و کاربردهای آن در هنر موسیقی پردازد. برای نیل به این هدف ابتدا 5 حوزه پرکاربرد تحقیقاتی و کاربردی هوش مصنوعی در موسیقی مدنظر قرار گرفته است. سپس اطلاعات تمامی اسناد علمی مرتبط با این 5 حوزه از پایگاه استنادی اسکوپوس استخراج شده است. در هر یک از 5 حوزه کشورهایی با بیشترین مقالات چاپ شده، دانشگاههایی که بیشترین مقالات را چاپ کردهاند و نویسندگان برتر استخراج گردید. علاوه بر این روند زمانی چاپ اسناد علمی مرتبط با هر حوزه مشخص گردید. در ادامه روشهای و الگوریتمهای مختلف هوش مصنوعی که در هر یک از حوزهها مورد استفاده قرار گرفتند مشخص شده و مقالات متناظر با هر یک از آنها نیز مشخص شده است. یادگیری ژرف در تمامی حوزهها در حال تغییر و بهبود نتایج بوده و سبب رشد کمی پژوهشهای علمی در حوزه موسیقی شده است. علاوه بر این هوش مصنوعی مولد در حیطه تولید موسیقی رشد چشمگیری را در رقم زده است. در نهایت نیز چالشها و موضوعات آینده تحقیقاتی حوزههای مختلف بررسی شده است. از مهمترین کارهایی که میتوان در آینده انجام داد، تهیه مجموعه دادگان مناسب برای حوزههای مختلف است.
چکیده انگلیسی:
"Despite its relatively short history, dating back to around 1960 when John McCarthy initiated the field, artificial intelligence (AI) has made significant inroads into nearly all domains, profoundly impacting them. Music is no exception. This research aims to systematically review the literature on AI and its applications in music, with the goal of understanding and introducing the various uses of AI in this art form. To achieve this, five primary research and application areas of AI in music were identified. Subsequently, data from all relevant scientific documents within these five areas was extracted from the Scopus database. For each of the five areas, the countries with the highest number of published articles, the universities with the most publications, and the top authors were identified. Additionally, the temporal trend of scientific document publication in each area was determined. Furthermore, the various AI methods and algorithms employed in each area were identified, along with the corresponding articles. Deep learning has been transforming and improving results in all areas, leading to a quantitative growth in scientific research within the field of music. Moreover, generative AI has experienced remarkable growth in the realm of music production. Finally, the challenges and future research topics of the different areas were examined. One of the most crucial tasks for the future is the creation of suitable datasets for various domains."
منابع و مأخذ:
اخشابی مجید، درتاج فریبرز. (1401). شناسایی متغیرهای میانجی در رابطه میان آموزش موسیقی و افزایش خلاقیت. فصلنامه علمی پژوهشی علوم روانشناختی، شماره ۲۱ ،۸۶۶-۸۵۳.
استخریان, امیر رضا, آزرم, الهام. (1400). مرور سیستماتیک مطالعات مرتبط با هوش مصنوعی و اینترنت اشیاء در شرکت های کوچک و متوسط(SME). اولین کنفرانس بین المللی برق و کامپیوتر، مکانیک و هوش مصنوعی.
بافنده, علی., شمول, داوود. (سال ششم، شماره 15، 1387). هوش مصنوعی و کاربرد آن در مدیریت. فروغ تدبیر, 44-49.
ترابی, علیرضا, احمدی زاد, آرمان, اصغر نیا, مرتضی (شماره 8، تابستان 1401). فرصتها و چالشها و روندهای جهانی فناوری تنظیم گری (مرور مبانی نظری). فصلنامه توسعه تکنولوژی صنعتی, 3-16.
حشمدار, اکرم, کردی, مراد. (بهار1401 ،سال چهارم، شماره 12). بررسی اثربخشی سیستم های هوش مصنوعی در کارکردهای منابع انسانی. فصلنامه پژوهش های معاصر در علوم مدیریت و حسابداری.
زیودار, زهره. (سال ششم، شماره 84، بهار 1401). کاربرد تکنیکهای هوش مصنوعی در حوزه حسابداری و مالی. فصلنامه رویکردهای پژوهشی نوین در مدیریت و حسابداری, 1572-1557.
کارکن بیرق, حبیب. (شماره 37، زمستان 1388). نگرش انتقادی به مسئه هوش مصنوعی. نشریه ذهن, 59-84.
کلاته رحمانی, راحله، چهارده چریکی, معصومه. (1389). هوش مصنوعی و کاربرد آن در حسابداری مالی. کنکاش, 135-140.
محمدعلی خلج, محمدحسین. (سال پنجم، شماره اول، بهار و تابستان 1393). درﻳﻔﻮس و ﺗﺎرﻳﺦ ﻓﻠﺴﻔﻲ ﻫﻮش ﻣﺼﻨﻮﻋﻲ. فصلنامه ﻏﺮب ﺷﻨﺎﺳﻲ بنیادی، پژوهشگاه علوم انسانی و مطالعات فرهنگی, 128-103.
نصیری مفخم، فریا. روغنی زاده، راضیه(1393).. ساخت هوشمند موسیقی با الگوریتم ژنتیک مبتنی بر جهش یکنواخت موتیف، فصلنامه هوش محاسباتی در مهندسی برق، 5(4)، 85-102.
Achar, B. A., Aiyappa, N. D., Akshaj, B., Thippeswamy, M. N., & Pillay, N. (2019). Activity-Based Music Classifier: A Supervised Machine Learning Approach for Curating Activity-Based Playlists. In Emerging Research in Computing, Information, Communication and Applications: ERCICA 2018, Volume 2 (pp. 185-198). Springer Singapore.
Adkins, S., Sarmento, P., & Barthet, M. (2023, April). LooperGP: a loopable sequence model for live coding performance using guitarpro tablature. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 3-19). Cham: Springer Nature Switzerland.
Akbari, M., Liang, J., & Cheng, H. (2018). A real-time system for online learning-based visual transcription of piano music. Multimedia tools and applications, 77, 25513-25535.
Araneda-Hernandez, M., Bravo-Marquez, F., Parra, D., & Cádiz, R. F. (2023). MUSIB: musical score inpainting benchmark. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 19.
Arnold, A. N., & Vairamuthu, S. (2019). Music recommendation using collaborative filtering and deep learning. Int. J. Innov. Technol. Explor. Eng.(IJITEE), 8(7), 964-968.
Arronte Alvarez, A., & Gómez, F. (2021). Motivic pattern classification of music audio signals combining residual and LSTM networks, International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), pp. 208–214.
Arronte Alvarez, A., & Gómez, F. (2021). Motivic pattern classification of music audio signals combining residual and LSTM networks, International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), pp. 208–214.
Ashraf, M., Abid, F., Din, I. U., Rasheed, J., Yesiltepe, M., Yeo, S. F., & Ersoy, M. T. (2023). A hybrid cnn and rnn variant model for music classification. Applied Sciences, 13(3), 1476.
Ashraf, M., Geng, G., Wang, X., Ahmad, F., & Abid, F. (2020). A globally regularized joint neural architecture for music classification. IEEE Access, 8, 220980-220989.
Ayata, D., Yaslan, Y., & Kamasak, M. E. (2018). Emotion based music recommendation system using wearable physiological sensors. IEEE transactions on consumer electronics, 64(2), 196-203.
Bai, J., Luo, K., Peng, J., Shi, J., Wu, Y., Feng, L., ... & Wang, Y. (2017, July). Music emotions recognition by cognitive classification methodologies. In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 121-129). IEEE.
Bai, Y. (2024, January). A music generation model based on Bi-LSTM. In International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023) (Vol. 12969, pp. 430-435). SPIE.
Banerjee, S., Rath, M., Swain, T., & Samant, T. (2022, April). Music generation using time distributed dense stateful char-RNNs. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE.
Barbancho, I., Tzanetakis, G., Barbancho, A. M., & Tardón, L. J. (2018). Discrimination between ascending/descending pitch arpeggios. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(11), 2194-2203.
Barnabò, G., Trappolini, G., Lastilla, L., Campagnano, C., Fan, A., Petroni, F., & Silvestri, F. (2023). Cycledrums: automatic drum arrangement for bass lines using cyclegan. Discover Artificial Intelligence, 3(1), 4.
Bayu, Q. D. A. P., Suyanto, S., & Arifianto, A. (2019, December). Hierarchical SVM-kNN to classify music emotion. In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 5-10). IEEE.
Benetos, E., & Dixon, S. (2011). Joint multi-pitch detection using harmonic envelope estimation for polyphonic music transcription. IEEE Journal of Selected Topics in Signal Processing, 5(6), 1111-1123.
Benetos, E., & Dixon, S. (2012). A shift-invariant latent variable model for automatic music transcription. Computer Music Journal, 36(4), 81-94.
Benetos, E., & Weyde, T. (2013). Explicit duration hidden markov models for multiple-instrument polyphonic music transcription.
Bhattarai, B., & Lee, J. (2024). Korean Pansori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding. Applied Sciences, 14(2), 492.
Bhattarai, B., Pandeya, Y. R., & Lee, J. (2020). Parallel stacked hourglass network for music source separation. IEEE Access, 8, 206016-206027.
Bian, W., Wang, J., Zhuang, B., Yang, J., Wang, S., & Xiao, J. (2019). Audio-based music classification with DenseNet and data augmentation. In PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part III 16 (pp. 56-65). Springer International Publishing.
Bihani, H., Bothe, S., Acharya, A., Desai, T., & Joglekar, P. (2023, April). Automatic Music Melody Generation Using LSTM and Markov Chain Model. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 249-257). Singapore: Springer Nature Singapore.
Bodily, P., & Ventura, D. (2022). Steerable Music Generation which Satisfies Long-Range Dependency Constraints. Transactions of the International Society for Music Information Retrieval, 5(1), 71-87.
Bogdanov, D., & Boyer, H. (2011). How much metadata do we need in music recommendation?: A subjective evaluation using preference sets. In Klapuri A, Leider C, editors. 12th International Society for Music Information Retrieval Conference (ISMIR 2011); 2011 Oct 24-28; Miami, USA. Montréal: ISMIR; 2011. p. 97-102. International Society for Music Information Retrieval (ISMIR).
Bogdanov, D., Haro, M., Fuhrmann, F., Xambó, A., Gómez, E., & Herrera, P. (2013). Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management, 49(1), 13-33.
Bressan, G. M., de Azevedo, B. C., & Lizzi, E. A. S. (2017). A decision tree approach for the musical genres classification. Applied Mathematics & Information Sciences, 11(6), 1703-1713.
Burlet, G., & Hindle, A. (2017). Isolated guitar transcription using a deep belief network. PeerJ Computer Science, 3, e109.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2016, October). Early handwritten music recognition with hidden markov models. In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 319-324). IEEE.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2017, November). Handwritten music recognition for mensural notation: Formulation, data and baseline results. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (Vol. 1, pp. 1081-1086). IEEE.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2019). Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation. Pattern Analysis and Applications, 22, 1573-1584.
Cañadas Quesada, F. J., Ruiz Reyes, N., Vera Candeas, P., Carabias, J. J., & Maldonado, S. (2010). A multiple-F0 estimation approach based on Gaussian spectral modelling for polyphonic music transcription. Journal of New Music Research, 39(1), 93-107.
Cao, B., Fukumori, T., & Yamashita, Y. (2023, October). Multi-Instruments Music Generation Based on Chord Input. In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) (pp. 1082-1083). IEEE.
Carthen, C. D., Le, V., Kelley, R., Kozubowski, T. J., & Harris Jr, F. C. (2017). Rewind: An Automatic Music Transcription Web Application. INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, 20.
Cazau, D., Revillon, G., Krywyk, J., & Adam, O. (2015). An investigation of prior knowledge in Automatic Music Transcription systems. The Journal of the Acoustical Society of America, 138(4), 2561-2573.
Cazau, D., Wang, Y., Adam, O., Wang, Q., & Nuel, G. (2017, October). Improving Note Segmentation in Automatic Piano Music Transcription Systems with a Two-State Pitch-Wise HMM Method. In ISMIR (pp. 523-530).
Cazau, D., Wang, Y., Chemillier, M., & Adam, O. (2016). An automatic music transcription system dedicated to the repertoires of the marovany zither. Journal of New Music Research, 45(4), 343-360.
Chakrabarty, S., Roy, S., & De, D. (2014). Pervasive diary in music rhythm education: a context-aware learning tool using genetic algorithm. In Advanced Computing, Networking and Informatics-Volume 1: Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014) (pp. 669-677). Cham: Springer International Publishing.
Chandna, P., Miron, M., Janer, J., & Gómez, E. (2017). Monoaural audio source separation using deep convolutional neural networks. In Latent Variable Analysis and Signal Separation: 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings 13 (pp. 258-266). Springer International Publishing.
Chang, H. Y., Huang, S. C., & Wu, J. H. (2017). A personalized music recommendation system based on electroencephalography feedback. Multimedia Tools and Applications, 76, 19523-19542.
Chang, J. W., Chiou, C. Y., Liao, J. Y., Hung, Y. K., Huang, C. C., Lin, K. C., & Pu, Y. H. (2021). Music recommender using deep embedding-based features and behavior-based reinforcement learning. Multimedia Tools and Applications, 1-28.
Chaudhary, D., Singh, N. P., & Singh, S. (2021). Emotion-Based Hindi Music Classification. In Proceedings of International Conference on Artificial Intelligence and Applications: ICAIA 2020 (pp. 553-563). Springer Singapore.
Chavare, S. R., Awati, C. J., & Shirgave, S. K. (2021, January). Smart recommender system using deep learning. In 2021 6th international conference on inventive computation technologies (ICICT) (pp. 590-594). IEEE.
Chen, C., & Li, Q. (2020). A multimodal music emotion classification method based on multifeature combined network classifier. Mathematical Problems in Engineering, 2020(1), 4606027.
Chen, H. (2024). Music Transcription Based on Deep Learning. Proceedings of 2023 International Conference on New Trends in Computational Intelligence, NTCI 2023, pp. 62–65.
Chen, J., Liu, Y., & Li, D. (2016). Dynamic group recommendation with modified collaborative filtering and temporal factor. Int. Arab J. Inf. Technol., 13(2), 294-301.
Chen, L., Shen, L., Yu, D., Wang, Z., Qian, K., Hu, B., ... & Yamamoto, Y. (2023, October). Multi-Track Music Generation with WGAN-GP and Attention Mechanisms. In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) (pp. 606-607). IEEE.
Chen, T. P., & Su, L. (2021). Attend to Chords: Improving Harmonic Analysis of Symbolic Music Using Transformer-Based Models. Trans. Int. Soc. Music. Inf. Retr., 4(1), 1-13.
Chen, Y., & Zheng, H. (2023). The application of HMM algorithm based music note feature recognition teaching in universities. Intelligent Systems with Applications, 20, 200277.
Cheng, R., & Tang, B. (2016). A music recommendation system based on acoustic features and user personalities. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers 20 (pp. 203-213). Springer International Publishing.
Choi, H. S., Lee, J., & Lee, K. (2020). Spec2spec: Towards the general framework of music processing using generative adversarial networks. Acoustical Science and Technology, 41(1), 160-165.
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017). Transfer learning for music classification and regression tasks. arXiv preprint arXiv:1703.09179.
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017, March). Convolutional recurrent neural networks for music classification. In 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP) (pp. 2392-2396). IEEE.
Choi, K., Hawthorne, C., Simon, I., Dinculescu, M., & Engel, J. (2020, November). Encoding musical style with transformer autoencoders. In International conference on machine learning (pp. 1899-1908). PMLR.
Comanducci, L., Gioiosa, D., Zanoni, M., Antonacci, F., & Sarti, A. (2023). Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 24.
Costa, Y. M., Oliveira, L. S., & Silla Jr, C. N. (2017). An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing, 52, 28-38.
Cuesta, H., & Gómez Gutiérrez, E. (2022). Voice assignment in vocal quartets using deep learning models based on pitch salience. Transactions of the International Society for Music Information Retrieval. 2022; 5 (1): 99-112.
Dai, J. (2022, February). Intelligent Music Style Classification System Based on K-Nearest Neighbor Algorithm and Artificial Neural Network. In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) (pp. 531-543). IEEE.
Dai, S., Jin, Z., Gomes, C., & Dannenberg, R. B. (2021). Controllable deep melody generation via hierarchical music structure
representation. Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021, pp. 143–150 Dai, Z., & Huang, X. (2022, September). Electronic Dance Music Classification Based on Machine Learning Methods. In 2022 International Conference on Electronics and Devices, Computational Science (ICEDCS) (pp. 351-354). IEEE.
Dalmazzo, D., Déguernel, K., & Sturm, B. L. (2024, March). The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 52-66). Cham: Springer Nature Switzerland.
Danyang, L., & Mingxin, G. (2021). Music Recommendation Method Based on Multi-Source Information Fusion. Data Analysis and Knowledge Discovery, 5(2), 94-105.
Darshna, P. (2018, January). Music recommendation based on content and collaborative approach & reducing cold start problem. In 2018 2nd international conference on inventive systems and control (ICISC) (pp. 1033-1037). IEEE.
De Boom, C., Van Laere, S., Verbelen, T., & Dhoedt, B. (2020). Rhythm, chord and melody generation for lead sheets using recurrent neural networks. In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II (pp. 454-461). Springer International Publishing. de la Fuente, C., Valero-Mas, J. J., Castellanos, F. J., & Calvo-Zaragoza, J. (2022). Multimodal image and audio music transcription. International Journal of Multimedia Information Retrieval, 11(1), 77-84.
Deepak, S., & Prasad, B. G. (2020, July). Music Classification based on Genre using LSTM. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 985-991). IEEE.
Demirel, E., Ahlbäck, S., & Dixon, S. (2020, July). Automatic lyrics transcription using dilated convolutional neural networks with self-attention. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Deng, Y., Lv, Y., Liu, M., & Lu, Q. (2015, December). A regression approach to categorical music emotion recognition. In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 257-261). IEEE.
Di, S., Jiang, Z., Liu, S., Wang, Z., Zhu, L., He, Z., ... & Yan, S. (2021, October). Video background music generation with controllable music transformer. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 2037-2045).
Donahue, C., Mao, H. H., Li, Y. E., Cottrell, G. W., & McAuley, J. (2019). LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. arXiv preprint arXiv:1907.04868.
Dong, H. W., Chen, K., Dubnov, S., McAuley, J., & Berg-Kirkpatrick, T. (2023, June). Multitrack music transformer. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Downie, J. S. (2003). Music information retrieval. Annual review of information science and technology, 37(1), 295-340.
Dua, M., Yadav, R., Mamgai, D., & Brodiya, S. (2020). An improved RNN-LSTM based novel approach for sheet music generation. Procedia Computer Science, 171, 465-474.
Egivenia, E., Ryanie Setiawan, G., Shania Mintara, S., & Suhartono, D. (2021, September). Classification of explicit music content based on lyrics, music metadata, and user annotation. In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology (pp. 265-270).
Elbir, A., & Aydin, N. (2020). Music genre classification and music recommendation by using deep learning. Electronics Letters, 56(12), 627-629.
Evangeline, D., Bhuvan, M., Parkavi, A., Manikarnike, A., Harikrishnan, M., & Adiga, B. S. (2023, October). Automated Music Synthesis Using Recurrent Neural Network and Long Short Term Memory. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (Vol. 1, pp. 1-5). IEEE.
Fan, Z. C., Lai, Y. L., & Jang, J. S. R. (2018, April). SVSGAN: singing voice separation via generative adversarial network. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 726-730). IEEE.
Fang, J. T., & Yang, C. W. (2022). SINGLE-CHANNEL MUSIC SOURCE SEPARATION BY HARMONIC STRUCTURE MODEL AND SUPPORT VECTOR MACHINE. 電機工程學刊, 29(2), 43-51.
Fang, Y., & Gui-fa, T. (2015). Visual music score detection with unsupervised feature learning method based on k-means. International Journal of Machine Learning and Cybernetics, 6, 277-287.
Farzaneh, M., & Mahdian Toroghi, R. (2020). Music generation using an interactive evolutionary algorithm. In Pattern Recognition and Artificial Intelligence: Third Mediterranean Conference, MedPRAI 2019, Istanbul, Turkey, December 22–23, 2019, Proceedings 3 (pp. 207-217). Springer International Publishing.
Ferano, F. C. A., Zahra, A., & Kusuma, G. P. (2023). Stacking ensemble learning for optical music recognition. Bulletin of Electrical Engineering and Informatics, 12(5), 3095-3104.
Fernando, R., Ranasinghe, P., Ranasinghe, U., Wijayakulasooriya, J., & Perera, P. (2023, September). Hybrid y-net architecture for singing voice separation. In 2023 31st European Signal Processing Conference (EUSIPCO) (pp. 156-160). IEEE.
Ferraro, A., Bogdanov, D., Yoon, J., Kim, K., & Serra, X. (2018). Automatic playlist continuation using a hybrid recommender system combining features from text and audio. In Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 1-5).
Fessahaye, F., Perez, L., Zhan, T., Zhang, R., Fossier, C., Markarian, R., ... & Oh, P. (2019, January). T-recsys: A novel music recommendation system using deep learning. In 2019 IEEE international conference on consumer electronics (ICCE) (pp. 1-6). IEEE.
Ford, C., Noel-Hirst, A., Cardinale, S., Loth, J., Sarmento, P., Wilson, E., ... & Bryan-Kinns, N. (2024, June). Reflection Across AI-based Music Composition. In Proceedings of the 16th Conference on Creativity & Cognition (pp. 398-412).
Fu, Z., Lu, G., Ting, K. M., & Zhang, D. (2010, August). Learning naive Bayes classifiers for music classification and retrieval. In 2010 20th international conference on pattern recognition (pp. 4589-4592). IEEE.
Fujieda, M., Murakami, T., & Ishida, Y. (2011). An approach to multi-pitch estimation using a support vector machine. International Journal of Computers and Applications, 33(3), 202-210.
Gale, E., Matthews, O., Costello, B. D. L., & Adamatzky, A. (2013). Beyond markov chains, towards adaptive memristor
network-based music generation. International Journal of Unconventional Computing, 10(3), 181–197 Gan, C., Huang, D., Chen, P., Tenenbaum, J. B., & Torralba, A. (2020). Foley music: Learning to generate music from videos. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 (pp. 758-775). Springer International Publishing.
Gao, H. (2022). Automatic recommendation of online music tracks based on deep learning. Mathematical Problems in Engineering, 2022(1), 5936156.
Garanayak, M., Nayak, S. K., Sangeetha, K., Choudhury, T., & Shitharth, S. (2022). Content and Popularity-Based Music Recommendation System. International Journal of Information System Modeling and Design (IJISMD), 13(7), 1-14.
Garani, S. S., & Seshadri, H. (2019). An algorithmic approach to South Indian classical music. Journal of Mathematics and Music, 13(2), 107-134.
Gillick, J., Yang, J., Cella, C. E., & Bamman, D. (2021). Drumroll Please: Modeling Multi-Scale Rhythmic Gestures with Flexible Grids. Transactions of the International Society for Music Information Retrieval, 4(1), 156-167.
Goel, K., Vohra, R., & Sahoo, J. K. (2014). Polyphonic music generation by modeling temporal dependencies using a rnn-dbn. In Artificial Neural Networks and Machine Learning–ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings 24 (pp. 217-224). Springer International Publishing.
Goel, N., Srivastava, T., Yuvraj, Monika, & Koundal, D. (2022, December). A Piano Tune Generation Model Using Deep Learning. In International Conference on Soft Computing and Pattern Recognition (pp. 424-432). Cham: Springer Nature Switzerland.
Gómez de Silva Garza, A., & Herrera González, E. (2012). Music Style Analysis Using the Random Forest Algorithm. In DS 73-2 Proceedings of the 2nd International conference on Design Creativity Volume 2 (pp. 343-350).
Grekow, J. (2023). Generating Polyphonic Symbolic Emotional Music in the Style of Bach Using Convolutional Conditional Variational Autoencoder. IEEE Access, 11, pp. 93019–93031
Gupta, C., Li, H., & Goto, M. (2022). Deep learning approaches in topics of singing information processing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2422-2451.
Gupta, K., Srivastava, K., Singh, K., & Pillai, R. (2023, July). Music Generation and Retrieval Using LSTM and VAE. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Gupta, P., & Dwivedi, P. (2023, December). Advancements in Music Generation: Leveraging RNN and LSTM Networks for Automated Composition. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 434-439). IEEE.
Hadjeres, G., & Nielsen, F. (2020). Anticipation-RNN: Enforcing unary constraints in sequence generation, with application to interactive music generation. Neural Computing and Applications, 32(4), 995-1005.
Han, Y., Kim, J., & Lee, K. (2016). Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(1), 208-221.
Hariri, N., Mobasher, B., & Burke, R. (2012, November). Using social tags to infer context in hybrid music recommendation. In Proceedings of the twelfth international workshop on Web information and data management (pp. 41-48).
He, Q. (2022). A Music Genre Classification Method Based on Deep Learning. Mathematical Problems in Engineering, 2022(1), 9668018.
Hernandez-Olivan, C., Zay Pinilla, I., Hernandez-Lopez, C., & Beltran, J. R. (2021). A comparison of deep learning methods for timbre analysis in polyphonic automatic music transcription. Electronics, 10(7), 810.
Highams, L., & Olszewska, J. I. (2023, July). Markov-chain-based musical creative intelligent agent passing successfully the Turing Test. In 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES) (pp. 000215-000220). IEEE.
Hofmann, D. M. (2015). A genetic programming approach to generating musical compositions. In Evolutionary and Biologically Inspired Music, Sound, Art and Design: 4th International Conference, EvoMUSART 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings 4 (pp. 89-100). Springer International Publishing.
Hsiao, W. Y., Liu, J. Y., Yeh, Y. C., & Yang, Y. H. (2021, May). Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 178-186).
Hsu, C., & Greer, R. (2023, December). Bridging Subjectivity and Objectivity in Evaluation of Machine-Generated Jazz Music: A Multimetric Approach. In 2023 IEEE International Symposium on Multimedia (ISM) (pp. 232-237). IEEE.
Hu, J., Ge, Z., & Wang, X. (2022). [Retracted] The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning. Computational Intelligence and Neuroscience, 2022(1), 3847415.
Hu, Z., Liu, Y., Chen, G., & Liu, Y. (2022). Can machines generate personalized music? a hybrid favorite-aware method for user preference music transfer. IEEE Transactions on Multimedia, 25, 2296-2308.
Hu, Z., Ma, X., Liu, Y., Chen, G., Liu, Y., & Dannenberg, R. B. (2023). The beauty of repetition: an algorithmic composition model with motif-level repetition generator and outline-to-music generator in symbolic music generation. IEEE Transactions on Multimedia.
Huang, J., Huang, X., Yang, L., & Tao, Z. (2024). Dance-Conditioned Artistic Music Generation by Creative-GAN. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 107(5), 836-844.
Huang, W., Xue, Y., Xu, Z., Peng, G., & Wu, Y. (2022). Polyphonic music generation generative adversarial network with Markov decision process. Multimedia Tools and Applications, 81(21), 29865-29885.
Huang, W., Yu, Y., Xu, H., Su, Z., & Wu, Y. (2023). Hyperbolic music transformer for structured music generation. IEEE Access, 11, 26893-26905.
Hulme, E., Marshall, D., Sidorov, K., & Jones, A. (2024, June). Acoustic Classification of Guitar Tunings with Deep Learning. In Proceedings of the 11th International Conference on Digital Libraries for Musicology (pp. 6-14).
Hung, Y. K., Pei, Y., & Li, J. (2023, July). Single-to-Multi Music Track Composition Using Interactive Chaotic Evolution. In International Conference on Frontier Computing (pp. 211-221). Singapore: Springer Nature Singapore.
Hwang, S. R., & Park, Y. C. (2020). Chord-based stepwise Korean Trot music generation technique using RNN-GAN. The Journal of the Acoustical Society of Korea, 39(6), 622-628.
Imasato, N., Miyazawa, K., Duncan, C., & Nagai, T. (2023). Using a language model to generate music in its symbolic domain while controlling its perceived emotion. IEEE Access, 11, 52412-52428.
Jain, N., Sah, N. K., Hawari, J., & Khare, S. (2024, April). Music Recommendation System based on Big Data using Pyspark. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.
Jakubik, J. (2018). Evaluation of gated recurrent neural networks in music classification tasks. In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology–ISAT 2017: Part I (pp. 27-37). Springer International Publishing.
Jaques, N., Gu, S., Turner, R. E., & Eck, D. (2017). Tuning recurrent neural networks with reinforcement learning. 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Jeyalakshmi, C., Murugeshwari, B., & Karthick, M. (2018, August). HMM and K-NN based automatic musical instrument recognition. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on (pp. 350-355). IEEE.
Ji, S., & Yang, X. (2023). Emomusictv: Emotion-conditioned symbolic music generation with hierarchical transformer vae. IEEE Transactions on Multimedia, 26, 1076-1088.
Ji, S., Yang, X., Luo, J., & Li, J. (2023). Rl-chord: Clstm-based melody harmonization using deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems.
Jiang, J., Xia, G. G., Carlton, D. B., Anderson, C. N., & Miyakawa, R. H. (2020, May). Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 516-520). IEEE.
Jiang, J., Xia, G. G., Carlton, D. B., Anderson, C. N., & Miyakawa, R. H. (2020, May). Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 516-520). IEEE.
Jin, C., Wang, T., Li, X., Tie, C. J. J., Tie, Y., Liu, S., ... & Huang, S. (2022). A transformer generative adversarial network for multi‐track music generation. CAAI Transactions on Intelligence Technology, 7(3), 369-380.
Jin, C., Wu, F., Wang, J., Liu, Y., Guan, Z., & Han, Z. (2022). MetaMGC: a music generation framework for concerts in metaverse. EURASIP journal on audio, speech, and music processing, 2022(1), 31.
Jing, L. (2024). Evolutionary Deep Learning for Sequential Data Processing in Music Education. Informatica, 48(8).
Jinming, Y. U., Zhuang, C. H. E. N., & Han, H. A. I. (2023). Multi-style Chord Music Generation Based on Artificial Neural Network. Journal of Donghua University (English Edition), 40(4).
Joel, J. S., Thompson, B. E., Thomas, S. R., Kumar, T. R., Prince, S., & Bini, D. (2023, April). Emotion based Music Recommendation System using Deep Learning Model. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 227-232). IEEE.
Johanson, B., Poli, R. (1998). GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters”, Genetic Prog. Proc. of the Third Annual Conf. CSRP.
Johnson, D., & Tzanetakis, G. (2015, August). Guitar model recognition from single instrument audio recordings. In 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (pp. 370-375). IEEE.
Joshi, S., Jain, T., & Nair, N. (2021, July). Emotion based music recommendation system using LSTM-CNN architecture. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 01-06). IEEE.
Kadandale, V. S., Montesinos, J. F., Haro, G., & Gómez, E. (2020, September). Multi-channel u-net for music source separation. In 2020 IEEE 22nd international workshop on multimedia signal processing (MMSP) (pp. 1-6). IEEE.
Kaliakatsos-Papakostas, M. A., Floros, A., & Vrahatis, M. N. (2016). Interactive music composition driven by feature evolution. SpringerPlus, 5, 1-38.
Kang, J., Poria, S., & Herremans, D. (2024). Video2music: Suitable music generation from videos using an affective multimodal transformer model. Expert Systems with Applications, 249, 123640.
Kang, S. I., & Lee, S. (2018). Improvement of speech/music classification for 3GPP EVS Based on LSTM. Symmetry, 10(11), 605.
Kasif, A., Sevgen, S., Ozcan, A., & Catal, C. (2024). Hierarchical multi-head attention LSTM for polyphonic symbolic melody generation. Multimedia Tools and Applications, 83(10), 30297-30317.
Kaushik, P., Rathore, S. P. S., Chahal, K., Saraf, S., Chauhan, G. S., & Kumar, P. (2024, March). RhythmQuest: Unifying Indian Music Classification and Prediction with Hybrid Deep Learning Techniques. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1-6). IEEE.
Keerti, G., Vaishnavi, A. N., Mukherjee, P., Vidya, A. S., Sreenithya, G. S., & Nayab, D. (2022). Attentional networks for music generation. Multimedia Tools and Applications, 81(4), 5179-5189.
Khine, K. K., & Su, C. (2024, May). Acoustic Scene Classification Using Deep C-RNN Based on Log Mel Spectrogram and Gammatone Frequency Cepstral Coefficients Features. In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-6). IEEE.
Khonglah, B. K., & Prasanna, S. M. (2015, December). Speech/music classification using vocal tract constriction aspect of speech. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
Kim, J., & Kang, H. G. (2023). Contrastive learning based deep latent masking for music source separation. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023-August, pp. 3709–3713.
Kim, J., Kim, K., You, K. H., & Lee, J. H. (2012). An approach for music recommendation using content-based analysis and collaborative filtering. International Information Institute (Tokyo). Information, 15(5), 1985.
Kolozali, Ş., Barthet, M., Fazekas, G., & Sandler, M. (2013). Automatic ontology generation for musical instruments based on audio analysis. IEEE transactions on audio, speech, and language processing, 21(10), 2207-2220.
Kong, M., Fan, L., Xu, S., Li, X., Hou, M., & Cao, C. (2024, April). Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 105-117). Singapore: Springer Nature Singapore.
Kopel, M., Antczak, D., & Walczyński, M. (2023, July). Generating Music for Video Games with Real-Time Adaptation to Gameplay Pace. In Asian Conference on Intelligent Information and Database Systems (pp. 261-272). Singapore: Springer Nature Singapore.
Kozlov, D., Bakulin, M., Pavlov, S., Zuev, A., Krylova, M., & Kharchikov, I. (2023, June). Learning Properties of Holomorphic Neural Networks of Dual Variables. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Krause, M., Weiß, C., & Müller, M. (2023, June). Soft dynamic time warping for multi-pitch estimation and beyond. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Krishna, A. S., Rajkumar, P. V., Saishankar, K. P., & John, M. (2011, January). Identification of carnatic raagas using hidden markov models. In 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 107-110). IEEE.
Krishna, B. S., Chiluvuri, S. S. R. S., Kosuri, S. S. H., & Chimbili, S. S. H. (2023, December). The Application of Long Short-Term Memory and Transformers for Music Generation. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4475-4478). IEEE.
Kumar, C., Kumar, M., & Jindal, A. (2023, May). Session-based song recommendation using recurrent neural network. In Machine Intelligence Techniques for Data Analysis and Signal Processing: Proceedings of the 4th International Conference MISP 2022, Volume 1 (pp. 719-728). Singapore: Springer Nature Singapore.
Kumar, R., Luo, Y., & Mesgarani, N. (2018, September). Music Source Activity Detection and Separation Using Deep Attractor Network. In Interspeech (pp. 347-351).
Kumar, S., Gudiseva, K., Iswarya, A., Rani, S., Prasad, K., & Sharma, Y. K. (2022, October). Automatic music generation system based on RNN architecture. In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 294-300). IEEE.
Kumble, A., Medatati, S., & Bhatt, A. (2023, February). Emotion-Based Music Recommendation. In International Conference on Advanced Computational and Communication Paradigms (pp. 147-155). Singapore: Springer Nature Singapore.
Kurniawan, F. (2011, July). Automatic music classification for Dangdut and campursari using Naïve Bayes. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics (pp. 1-6). IEEE.
Kurniawati, A., Yuniarno, E. M., Suprapto, Y. K., & Ikhsan Soewidiatmaka, A. N. (2023). Automatic note generator for Javanese gamelan music accompaniment using deep learning. International Journal of Advances in Intelligent Informatics, 9(2).
Kurniawati, A., Yuniarno, E. M., Suprapto, Y. K., & Ikhsan Soewidiatmaka, A. N. (2023). Automatic note generator for Javanese gamelan music accompaniment using deep learning. International Journal of Advances in Intelligent Informatics, 9(2).
Kwon, T., Jeong, D., & Nam, J. (2017). Audio-to-score alignment of piano music using RNN-based automatic music transcription. arXiv preprint arXiv:1711.04480.
Kwon, T., Jeong, D., & Nam, J. (2017). Audio-to-score alignment of piano music using RNN-based automatic music transcription. arXiv preprint arXiv:1711.04480.
Lazar, N., & Chithra, K. (2021). Comprehensive bibliometric mapping of publication trends in the development of Building Sustainability Assessment Systems. Environment, Development and Sustainability, 23(4), 4899–4923.
Lee, C. T., Yang, Y. H., & Chen, H. H. (2012). Multipitch estimation of piano music by exemplar-based sparse representation. IEEE Transactions on Multimedia, 14(3), 608-618.
Lee, D. H., Park, J. W., & Seo, Y. S. (2018). Multiple Regression-Based Music Emotion Classification Technique. KIPS Transactions on Software and Data Engineering, 7(6), 239-248.
Lee, J. H., Choi, H. S., & Lee, K. (2019). Audio query-based music source separation. Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, pp. 878–885
Lee, J. H., Kim, J. Y., Jeong, D. K., & Kim, H. G. (2022). Music classification system through emotion recognition based on regression model of music signal and electroencephalogram features. The Journal of the Acoustical Society of Korea, 41(2), 115-121.
Li, C., & Zuo, X. (2024). Classical music recommendation algorithm on art market audience expansion under deep learning. Journal of Intelligent Systems, 33(1), 20230351.
Li, F. (2024). Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence. The Journal of Supercomputing, 80(5), 6068-6092.
Li, G., Ding, S., & Li, Y. (2021, October). Novel LSTM-gan based music generation. In 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1-6). IEEE.
Li, J., Han, L., Wang, X., Wang, Y., Xia, J., Yang, Y., ... & Yan, H. (2024). A hybrid neural network model based on optimized margin softmax loss function for music classification. Multimedia Tools and Applications, 83(15), 43871-43906.
Li, P., Liang, T. M., Cao, Y. M., Wang, X. M., Wu, X. J., & Lei, L. Y. (2024). A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning. Applied Intelligence, 54(1), 80-94.
Li, P., Song, T., & Hu, J. (2023, August). Audio classification based on audio WSOLA and CNN algorithm. In Second International Conference on Electronic Information Technology (EIT 2023) (Vol. 12719, pp. 1145-1149). SPIE.
Li, P., Wu, J., & Ji, Z. (2023). HRPE: Hierarchical Relative Positional Encoding for Transformer-Based Structured Symbolic Music Generation. In Summit on Music Intelligence (pp. 122-134). Singapore: Springer Nature Singapore.
Li, S., & Sung, Y. (2021). INCO-GAN: variable-length music generation method based on inception model-based conditional GAN. Mathematics, 9(4), 387.
Li, S., & Sung, Y. (2023). MelodyDiffusion: chord-conditioned melody generation using a transformer-based diffusion model. Mathematics, 11(8), 1915.
Li, S., & Sung, Y. (2023). Transformer-based seq2seq model for chord progression generation. Mathematics, 11(5), 1111.
Li, T., Chen, J., Hou, H., & Li, M. (2021, January). Sams-net: A sliced attention-based neural network for music source separation. In 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP) (pp. 1-5). IEEE.
Li, X., & Niu, Y. (2022). [Retracted] Research on Chord‐Constrained Two‐Track Music Generation Based on Improved GAN Networks. Scientific Programming, 2022(1), 5882468.
Li, X., Wang, K., Soraghan, J., & Ren, J. (2020). Fusion of Hilbert-Huang transform and deep convolutional neural network for predominant musical instruments recognition. In Artificial Intelligence in Music, Sound, Art and Design: 9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings 9 (pp. 80-89). Springer International Publishing.
Li, X., Yan, Y., Soraghan, J., Wang, Z., & Ren, J. (2023). A music cognition–guided framework for multi-pitch estimation. Cognitive computation, 15(1), 23-35.
Li, Y., & Liu, X. (2022). An Intelligent Music Production Technology Based on Generation Confrontation Mechanism. Computational Intelligence and Neuroscience, 2022(1), 5083146.
Li, Y., Li, X., Lou, Z., & Chen, C. (2022). Long short-term memory-based music analysis system for music therapy. Frontiers in Psychology, 13, 928048.
Li, Z., Huang, Q., Yang, X., Chen, Q., & Zhang, L. (2024). Automatic Composition System Based on Transformer-XL. Applied Sciences, 14(13), 5765.
Li, Z., Peng, H., Tan, S., & Zhu, F. (2023, September). Music classification with convolutional and artificial neural network. In Journal of Physics: Conference Series (Vol. 2580, No. 1, p. 012059). IOP Publishing.
Liang, Q., & Wang, Y. (2024). Drawlody: Sketch-Based Melody Creation with Enhanced Usability and Interpretability. IEEE Transactions on Multimedia.
Liao, X., Li, X., Xu, Q., Wu, H., & Wang, Y. (2020). Improving ant collaborative filtering on sparsity via dimension reduction. Applied Sciences, 10(20), 7245.
Liao, Y., Xu, H., & Xu, K. (2023, July). MusicGAIL: A Generative Adversarial Imitation Learning Approach for Music Generation. In CAAI International Conference on Artificial Intelligence (pp. 505-516). Singapore: Springer Nature Singapore.
Lim, C., & Chang, J. H. (2012). Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion. IET signal processing, 6(4), 335-340.
Lim, C., Lee, S. R., & Chang, J. H. (2012). Efficient implementation of an SVM-based speech/music classifier by enhancing temporal locality in support vector references. IEEE Transactions on Consumer Electronics, 58(3), 898-904.
Lim, Y. Q., Chan, C. S., & Loo, F. Y. (2020, July). Style-conditioned music generation. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
Lin, C. Y., Wang, L. C., & Tsai, K. H. (2018). Hybrid real-time matrix factorization for implicit feedback recommendation systems. Ieee Access, 6, 21369-21380.
Linlin, P. (2022). Tchaikovsky music recommendation algorithm based on deep learning. Mobile Information Systems, 2022(1), 1265451.
Liu, H. (2023). A novel Internet of things-enabled intelligent piano musical identification system using support vector machine. Soft Computing, 27(24), 19125-19142.
Liu, H., & Zhao, C. (2024). A Deep Learning Algorithm for Music Information Retrieval Recommendation System, Computer-Aided Design and Applications, 21(S13), 1–16.
Liu, H., Xie, L., Wu, J., & Yang, G. (2020). Channel-wise subband input for better voice and accompaniment separation on high resolution music. arXiv preprint arXiv:2008.05216.
Liu, X. (2022). Research on Music Genre Recognition Based on Improved Naive Bayes Algorithm. Mobile Information Systems, 2022(1), 1909928.
Liu, Y. (2023). Optimization of Teaching Innovation Path of Music Education in Colleges and Universities Based on the Stochastic Process Model. Applied Mathematics and Nonlinear Sciences, 9(1).
Liu, Y., Jin, Z., Cai, L., Li, J. (2011). Singing annotation approach for solfege evaluation. Qinghua Daxue Xuebao/Journal of Tsinghua University, 51(12), pp. 1808–1813
Liutkus, A., Cıfka, O., Wu, S. L., Simsekli, U., Yang, Y. H., & Richard, G. (2021, July). Relative positional encoding for transformers with linear complexity. In International Conference on Machine Learning (pp. 7067-7079). PMLR.
Lluís, F., Pons, J., & Serra, X. (2018). End-to-end music source separation: Is it possible in the waveform domain?. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019-September, pp. 4619–4623
Lopez-Rincon, O., Starostenko, O., & Lopez-Rincon, A. (2022). Algorithmic music generation by harmony recombination with genetic algorithm. Journal of Intelligent & Fuzzy Systems, 42(5), 4411-4423.
Lordelo, C., Benetos, E., Dixon, S., Ahlbäck, S., & Ohlsson, P. (2020). Adversarial unsupervised domain adaptation for harmonic-percussive source separation. IEEE Signal Processing Letters, 28, 81-85.
Lu, M., & Deng, F. (2021). [Retracted] Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching. Security and Communication Networks, 2021(1), 3140341.
Luo, J., Yang, X., Ji, S., & Li, J. (2020). MG-VAE: deep Chinese folk songs generation with specific regional styles. In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers (pp. 93-106). Springer Singapore.
Luo, Y., & Yu, J. (2023). Music source separation with band-split RNN. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1893-1901.
Luo, Y., Chen, Z., Hershey, J. R., Le Roux, J., & Mesgarani, N. (2017, March). Deep clustering and conventional networks for music separation: Stronger together. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 61-65). IEEE.
Maccarini, F., Oudin, M., Giraud, M., & Levé, F. (2024, March). Co-creative orchestration of Angeles with layer scores and orchestration plans. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 228-245). Cham: Springer Nature Switzerland.
Magadum, H., Azad, H. K., & Patel, H. (2024). Music recommendation using dynamic feedback and content-based filtering. Multimedia Tools and Applications, 1-20.
Magron, P., & Févotte, C. (2022). Neural content-aware collaborative filtering for cold-start music recommendation. Data Mining and Knowledge Discovery, 36(5), 1971-2005.
Mahanta, S. K., Basisth, N. J., Halder, E., Khilji, A. F. U. R., & Pakray, P. (2023). Exploiting cepstral coefficients and CNN for efficient musical instrument classification. Evolving Systems, 1-13.
Mahardhika, F., Warnars, H. L. H. S., & Heryadi, Y. (2018, September). Indonesian's dangdut music classification based on audio features. In 2018 Indonesian association for pattern recognition international conference (INAPR) (pp. 99-103). IEEE. Majidi, M., & Toroghi, R. M. (2023). A combination of multi-objective genetic algorithm and deep learning for music harmony generation. Multimedia Tools and Applications, 82(2), 2419-2435.
Makris, D., Agres, K. R., & Herremans, D. (2021, July). Generating lead sheets with affect: A novel conditional seq2seq framework. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Malheiro, R., Panda, R., Gomes, P., & Paiva, R. P. (2016). Emotionally-relevant features for classification and regression of music lyrics. IEEE Transactions on Affective Computing, 9(2), 240-254.
Manilow, E., Wichern, G., Seetharaman, P., & Le Roux, J. (2019, October). Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity. In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (pp. 45-49). IEEE.
Mao, Y., Zhong, G., Wang, H., & Huang, K. (2022). Music-CRN: An efficient content-based music classification and recommendation network. Cognitive Computation, 14(6), 2306-2316.
Matić, D. (2010). A genetic algorithm for composing music. Yugoslav Journal of Operations Research, 20(1), 157-177. McLeod, A., Schramm, R., Steedman, M., & Benetos, E. (2017). Automatic transcription of polyphonic vocal music. Applied Sciences, 7(12), 1285.
Miao, B. C., Guo, W. A., & Wang, L. (2019). A polyphony music generation system based on latent features and a recurrent neural network. CAAI Trans Intell Syst, 14(1), 158-164.
Mimilakis, S. I., Drossos, K., Virtanen, T., & Schuller, G. (2017, September). A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation. In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
Min, J., Liu, Z., Wang, L., Li, D., Zhang, M., & Huang, Y. (2022). Music generation system for adversarial training based on deep learning. Processes, 10(12), 2515.
Miron, M., Janer Mestres, J., & Gómez Gutiérrez, E. (2017). Generating data to train convolutional neural networks for classical music source separation. In Lokki T, Pätynen J, Välimäki V, editors. Proceedings of the 14th Sound and Music Computing Conference; 2017 Jul 5-8; Espoo, Finland. Aalto: Aalto University; 2017. p. 227-33.. Aalto University.
Miron, M., Janer Mestres, J., & Gómez Gutiérrez, E. (2017). Monaural score-informed source separation for classical music using convolutional neural networks. In Hu X, Cunningham SJ, Turnbull D, Duan Z. ISMIR 2017. 18th International Society for Music Information Retrieval Conference; 2017 Oct 23-27; Suzhou, China.[Canada]: ISMIR; 2017. p. 55-62.. International Society for Music Information Retrieval (ISMIR).
Mohan, G. B., Kumar, R. P., Korrayi, S., Harshitha, M., Chaithanya, B. S. S., Saiteja, K., & Rohan, G. V. (2024, February). Emotion-Based Music Recommendation System-A Deep Learning Approach. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-7). IEEE.
Mohanty, M. N., Tripathy, V., & Pattanaik, R. K. (2023, June). Music Regeneration with RNN Architecture Using LSTM. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) (pp. 538-542). IEEE.
Mou, L., Sun, Y., Tian, Y., Sun, Y., Liu, Y., Zhang, Z., ... & Jain, R. (2023, July). MemoMusic 3.0: considering context at music recommendation and combining music theory at music generation. In 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 296-301). IEEE.
Muhamed, A., Li, L., Shi, X., Yaddanapudi, S., Chi, W., Jackson, D., ... & Smola, A. J. (2021, May). Symbolic music generation with transformer-gans. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 1, pp. 408-417).
Mukherjee, H., Dhar, A., Ghosh, M., Obaidullah, S. M., Santosh, K. C., Phadikar, S., & Roy, K. (2020). Music chord inversion shape identification with LSTM-RNN. Procedia Computer Science, 167, 607-615.
Mukhopadhyay, S., Kumar, A., Parashar, D., & Singh, M. (2024). Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches. Revue d'Intelligence Artificielle, 38(1).
Musto, C., Semeraro, G., Lops, P., De Gemmis, M., & Narducci, F. (2012). Leveraging social media sources to generate personalized music playlists. In E-Commerce and Web Technologies: 13th International Conference, EC-Web 2012, Vienna, Austria, September 4-5, 2012. Proceedings 13 (pp. 112-123). Springer Berlin Heidelberg.
Mycka, J., Żychowski, A., & Mańdziuk, J. (2023). Toward human-level tonal and modal melody harmonizations. Journal of Computational Science, 67, 101963.
Mysore, G. J., Smaragdis, P., & Raj, B. (2010, September). Non-negative hidden Markov modeling of audio with application to source separation. In International Conference on Latent Variable Analysis and Signal Separation (pp. 140-148). Berlin, Heidelberg: Springer Berlin Heidelberg.
Nabizadeh, A. H., Jorge, A. M., Tang, S., & Yu, Y. (2016, July). Predicting user preference based on matrix factorization by exploiting music attributes. In Proceedings of the ninth international c* conference on computer science & software engineering (pp. 61-66).
Nakamura, E., Benetos, E., Yoshii, K., & Dixon, S. (2018, April). Towards complete polyphonic music transcription: Integrating multi-pitch detection and rhythm quantization. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 101-105). IEEE.
Nakamura, E., Cuvillier, P., Cont, A., Ono, N., & Sagayama, S. (2015, October). Autoregressive hidden semi-Markov model of symbolic music performance for score following. In 16th International Society for Music Information Retrieval Conference (ISMIR).
Nakamura, E., Itoyama, K., & Yoshii, K. (2016, August). Rhythm transcription of MIDI performances based on hierarchical Bayesian modelling of repetition and modification of musical note patterns. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 1946-1950). IEEE.
Nakano, M., Le Roux, J., Kameoka, H., Nakamura, T., Ono, N., & Sagayama, S. (2011, October). Bayesian nonparametric spectrogram modeling based on infinite factorial infinite hidden Markov model. In 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (pp. 325-328). IEEE.
Narducci, F., De Gemmis, M., & Lops, P. (2015, September). A general architecture for an emotion-aware content-based recommender system. In Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015 (pp. 3-6).
Narkhede, N., Mathur, S., Bhaskar, A., & Kalla, M. (2024). Music genre classification and recognition using convolutional neural network. Multimedia Tools and Applications, 1-16.
Navarro-Caceres, M., Oliveira, H. G., Martins, P., & Cardoso, A. (2020). Integration of a music generator and a song lyrics generator to create Spanish popular songs. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4421-4437.
Ning, Hui, Li, Qian,(2020),Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm, Complexity,6643888.
Niu, Y. (2022). Collaborative Filtering‐Based Music Recommendation in Spark Architecture. Mathematical Problems in Engineering, 2022(1), 9050872.
Niyazov, A., Mikhailova, E., & Egorova, O. (2021, May). Content-based music recommendation system. In 2021 29th Conference of Open Innovations Association (FRUCT) (pp. 274-279). IEEE.
Nuo, M., Han, X., & Zhang, Y. (2023, November). Contrastive learning-based music recommendation model. In International Conference on Neural Information Processing (pp. 370-382). Singapore: Springer Nature Singapore.
Ojima, Y., Nakamura, E., Itoyama, K., & Yoshii, K. (2018). Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models. APSIPA Transactions on Signal and Information Processing, 7, e14.
Omowonuola, V., Wilkerson, B., & Kher, S. (2022, October). Hybrid Context-Content Based Music Recommendation System. In Proceedings of the Future Technologies Conference (pp. 121-132). Cham: Springer International Publishing.
Oramas, S., Nieto, O., Sordo, M., & Serra, X. (2017, August). A deep multimodal approach for cold-start music recommendation. In Proceedings of the 2nd workshop on deep learning for recommender systems (pp. 32-37).
Pan, J., Yu, S., Zhang, Z., Hu, Z., & Wei, M. (2022). The Generation of Piano Music Using Deep Learning Aided by Robotic Technology. Computational Intelligence and Neuroscience, 2022(1), 8336616.
Pan, X., Song, B., Liu, C., & Zhang, H. (2019, July). Music detecting and recording system based on support vector machine. In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) (pp. 244-248). IEEE.
Papadopoulos G,Wiggins, G. (1999).AI Methods for Algorithmic Composition: A Survey, a Critical view and Future Prospects”, AISB Symposium on Musical Creativity, Edinburgh, UK.
Papadopoulos, A., Roy, P., & Pachet, F. (2016). Assisted lead sheet composition using flowcomposer. In Principles and Practice of Constraint Programming: 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings 22 (pp. 769-785). Springer International Publishing.
Park, J., Choi, K., Oh, S., Kim, L., & Park, J. (2023). Note-level singing melody transcription with transformers. Intelligent Data Analysis, 27(6), 1853-1871.
Patel, J., Padaria, A. A., Mehta, A., Chokshi, A., Patel, J. D., & Kapdi, R. (2023). ConCollA-A Smart Emotion-based Music Recommendation System for Drivers. Scalable Computing: Practice and Experience, 24(4), 919-939.
Patil, S. S., Patil, S. H., Pawar, A. M., Shandilya, R., Kadam, A. K., Jadhav, R. B., & Bewoor, M. S. (2023, November). Music Generation Using RNN-LSTM with GRU. In 2023 International Conference on Integration of Computational Intelligent System (ICICIS) (pp. 1-5). IEEE.
Pavan, V., & Dhanalakshmi, R. (2024, May). Performance evaluation of music genre prediction system using convolutional neural network and random forest. In AIP Conference Proceedings (Vol. 2853, No. 1). AIP Publishing.
Pavan, V., Vickram, A. S., & Dhanalakshmi, R. (2024, May). Efficient prediction of music genre using support vector machine and decision tree. In AIP Conference Proceedings (Vol. 2853, No. 1). AIP Publishing.
Peng, W., Tang, Y., & Ouyang, Y. (2023, June). Design of Computer-Aided Music Generation Model Based on Artificial Intelligence Algorithm. In International Conference on Computational Finance and Business Analytics (pp. 229-237). Cham: Springer Nature Switzerland.
Pezzat-Morales, M., Perez-Meana, H., & Nakashika, T. (2023). Fast Jukebox: Accelerating Music Generation with Knowledge Distillation. Applied Sciences, 13(9), 5630.
Pulis, M., & Bajada, J. (2021, September). Siamese Neural Networks for Content-based Cold-Start Music Recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems (pp. 719-723).
Qian, J., Gao, Y., Wei, W., Zhao, J., & Li, W. (2022, September). SepMLP: An all-MLP architecture for music source separation. In Proceedings of the 9th Conference on Sound and Music Technology: Revised Selected Papers from CMST (pp. 31-41). Singapore: Springer Nature Singapore.
Qian, J., Liu, X., Yu, Y., & Li, W. (2023). Stripe-Transformer: deep stripe feature learning for music source separation. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 2.
Qiao, L., Tang, Q., & Sun, X. (2023, November). Music Recognition and Classification Recommendation Based on Convolutional Neural Network. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 1-7). IEEE.
Qin, Y., Xie, H., Ding, S., Tan, B., Li, Y., Zhao, B., & Ye, M. (2023). Bar transformer: a hierarchical model for learning long-term structure and generating impressive pop music. Applied Intelligence, 53(9), 10130-10148.
Quinto, R. J. M., Atienza, R. O., & Tiglao, N. M. C. (2017, November). Jazz music sub-genre classification using deep learning. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 3111-3116). IEEE.
Rahardwika, D. S., Rachmawanto, E. H., Sari, C. A., Irawan, C., Kusumaningrum, D. P., & Trusthi, S. L. (2020, September). Comparison of SVM, KNN, and NB classifier for genre music classification based on metadata. In 2020 international seminar on application for technology of information and communication (iSemantic) (pp. 12-16). IEEE.
Rajan, R., & Mohan, B. S. (2021). Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors. International Journal of Pattern Recognition and Artificial Intelligence, 35(13), 2151014.
Reddy, M. S., & Adilakshmi, T. (2014, January). Music recommendation system based on matrix factorization technique-SVD. In 2014 international conference on computer communication and informatics (pp. 1-6). IEEE.
Reddy, S. V., Krishna, V. R., Sapkal, R. J., Dhanke, J., Waghmare, S. P., & Kumar, K. (2024). A cutting-edge artificial intelligence paradigm for entertainment-infused music recommendations. Entertainment Computing, 51, 100717.
Ren, T., Wang, F., & Wang, H. (2020). A sequential naive Bayes method for music genre classification based on transitional information from pitch and beat. Statistics and Its Interface, 13(3), 361-371.
Richard, G., Chouteau, P., & Torres, B. (2024, April). A fully differentiable model for unsupervised singing voice separation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 946-950). IEEE.
Roy, P., & Pachet, F. (2013, June). Enforcing meter in finite-length markov sequences. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 27, No. 1, pp. 854-861).
Sadek, R. A., Khalifa, A. A., & Elfattah, M. M. (2021, December). Detecting digital stimulant music using bidirectional deep long short term memory. In 2021 9th international Japan-Africa conference on electronics, communications, and computations (JAC-ECC) (pp. 150-154). IEEE.
Sajad, S., Dharshika, S., & Meleet, M. (2021, September). Music generation for novices using Recurrent Neural Network (RNN). In 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE.
Sánchez-Moreno, D., González, A. B. G., Vicente, M. D. M., Batista, V. F. L., & García, M. N. M. (2016). A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Systems with Applications, 66, 234-244.
Sarmento, P., Kumar, A., Chen, Y. H., Carr, C. J., Zukowski, Z., & Barthet, M. (2023, April). GTR-CTRL: instrument and genre conditioning for guitar-focused music generation with transformers. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 260-275). Cham: Springer Nature Switzerland.
Scarpiniti, M., Sigismondi, E., Comminiello, D., & Uncini, A. (2023, September). A U-Net Based Architecture for Automatic Music Transcription. In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
Schulze, W., & Van Der Merwe, B. (2011). Music generation with Markov models. IEEE MultiMedia, 18(03), 78-85.
Schulze-Forster, K., Richard, G., Kelley, L., Doire, C. S., & Badeau, R. (2023). Unsupervised music source separation using differentiable parametric source models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1276-1289. Schwabe, M., & Heizmann, M. (2023). Improved Separation of Polyphonic Chamber Music Signals by Integrating Instrument Activity Labels. IEEE Access, 11, 42999-43007.
Schwabe, M., Murgul, S., & Heizmann, M. (2022). Dual task monophonic singing transcription. Journal of the Audio Engineering Society, 70(12), 1038-1047.
Seetharaman, P., Wichern, G., Venkataramani, S., & Le Roux, J. (2019, May). Class-conditional embeddings for music source separation. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 301-305). IEEE.
Senac, C., Pellegrini, T., Mouret, F., & Pinquier, J. (2017, June). Music feature maps with convolutional neural networks for music genre classification. In Proceedings of the 15th international workshop on content-based multimedia indexing (pp. 1-5).
Sgouros, T., Bousis, A., & Mitianoudis, N. (2022). An efficient short-time discrete cosine transform and attentive MultiResUNet framework for music source separation. IEEE Access, 10, 119448-119459.
Shen, C., Yao, V. Z., & Liu, Y. (2023, June). Everybody Compose: Deep Beats To Music. In Proceedings of the 14th Conference on ACM Multimedia Systems (pp. 353-357).
Shi, N., & Wang, Y. (2020). Symmetry in computer-aided music composition system with social network analysis and artificial neural network methods. Journal of Ambient Intelligence and Humanized Computing, 1-16.
Shih, Y. J., Wu, S. L., Zalkow, F., Müller, M., & Yang, Y. H. (2022). Theme transformer: Symbolic music generation with theme-conditioned transformer. IEEE Transactions on Multimedia, 25, 3495-3508.
Siddiquee, M. M. R., Rahman, M. S., Chowdhury, S. U. I., & Rahman, R. M. (2016). Association rule mining and audio signal processing for music discovery and recommendation. International Journal of Software Innovation (IJSI), 4(2), 71-87.
Siedenburg, K., Schädler, M. R., & Hülsmeier, D. (2019). Modeling the onset advantage in musical instrument recognition. The journal of the Acoustical Society of America, 146(6), EL523-EL529.
Sigtia, S., Benetos, E., & Dixon, S. (2016). An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(5), 927-939.
Simonetta, F., Ntalampiras, S., & Avanzini, F. (2021, October). Audio-to-score alignment using deep automatic music transcription. In 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP) (pp. 1-6). IEEE.
Simonetta, F., Ntalampiras, S., & Avanzini, F. (2021, October). Audio-to-score alignment using deep automatic music transcription. In 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP) (pp. 1-6). IEEE.
Singh, J., & Bohat, V. K. (2021, January). Neural network model for recommending music based on music genres. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE.
Singh, J., & Ratnawat, A. (2018, December). Algorithmic Music Generation for the stimulation of Musical Memory in Alzheimer’s. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1-4). IEEE.
Slizovskaia, O., Kim, L., Haro, G., & Gomez, E. (2019, May). End-to-end sound source separation conditioned on instrument labels. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 306-310). IEEE.
Soleymani, M., Aljanaki, A., Wiering, F., & Veltkamp, R. C. (2015, June). Content-based music recommendation using underlying music preference structure. In 2015 IEEE international conference on multimedia and expo (ICME) (pp. 1-6). IEEE.
Stasiak, B., & Papiernik, M. (2013). Melody-based approaches in music retrieval and recommendation systems. In Multimedia Services in Intelligent Environments: Recommendation Services (pp. 125-153). Heidelberg: Springer International Publishing.
Stefani, D., Peroni, S., & Turchet, L. (2022). A comparison of deep learning inference engines for embedded real-time audio classification. In Proceedings of the International Conference on Digital Audio Effects, DAFx (Vol. 3, pp. 256-263). MDPI (Multidisciplinary Digital Publishing Institute).
Steiner, P., Jalalvand, A., Stone, S., & Birkholz, P. (2021, January). Feature engineering and stacked echo state networks for musical onset detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9537-9544). IEEE.
Stoller, D., Durand, S., & Ewert, S. (2019, May). End-to-end lyrics alignment for polyphonic music using an audio-to-character recognition model. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 181-185). IEEE.
Su, J. H., Chang, W. Y., & Tseng, V. S. (2017). Effective social content-based collaborative filtering for music recommendation. Intelligent Data Analysis, 21(S1), S195-S216.
Su, J. H., Chin, C. Y., Liao, Y. W., Yang, H. C., Tseng, V. S., & Hsieh, S. Y. (2020). A personalized music recommender system using user contents, music contents and preference ratings. Vietnam Journal of Computer Science, 7(01), 77-92.
Sudarma, M., & Harsemadi, I. G. (2017). Design and analysis system of KNN and ID3 algorithm for music classification based on mood feature extraction. International Journal of Electrical and Computer Engineering, 7(1), 486.
Sun, J. (2022). Personalized music recommendation algorithm based on spark platform. Computational Intelligence and Neuroscience, 2022(1), 7157075.
Sunitha, M., & Adilakshmi, T. (2018). Music recommendation system with user-based and item-based collaborative filtering technique. In Networking Communication and Data Knowledge Engineering: Volume 1 (pp. 267-278). Springer Singapore.
Suprapto, Y. K., & Triwidyastuti, Y. (2015). Saron music transcription based on rhythmic information using hmm on gamelan orchestra. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(1), 103-117.
Tahmasebi, S., Gajȩcki, T., & Nogueira, W. (2020). Design and evaluation of a real-time audio source separation algorithm to remix music for cochlear implant users. Frontiers in Neuroscience, 14, 434.
Tanberk, S., & Tükel, D. B. (2021, January). Style-specific Turkish pop music composition with CNN and LSTM network. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000181-000185). IEEE.
Tang, H., & Chen, N. (2020). Combining CNN and broad learning for music classification. IEICE TRANSACTIONS on Information and Systems, 103(3), 695-701.
Tang, J., Yin, L., & Yu, J. (2023, June). Partially Trained Music Generation based on Transformer. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 1133-1138). IEEE.
Telila, Y., Cucinotta, T., & Bacciu, D. (2023). Automatic Music Transcription using Convolutional Neural Networks and Constant-Q Transform. In Ital-IA (pp. 526-531).
Thambi, S. V., Sreekumar, K. T., Kumar, C. S., & Raj, P. R. (2014, December). Random forest algorithm for improving the performance of speech/non-speech detection. In 2014 First International Conference on Computational Systems and Communications (ICCSC) (pp. 28-32). IEEE.
Tian, D., Chen, J., Gao, Z., & Pan, G. (2023, September). Linear Transformer-GAN: A Novel Architecture to Symbolic Music Generation. In International Conference on Artificial Neural Networks (pp. 451-463). Cham: Springer Nature Switzerland.
Tian, D., Chen, J., Gao, Z., & Pan, G. (2023, September). Linear Transformer-GAN: A Novel Architecture to Symbolic Music Generation. In International Conference on Artificial Neural Networks (pp. 451-463). Cham: Springer Nature Switzerland.
Tian, L., & Liu, F. (2023). Design of Music Heritage Database System Based on Collaborative Filtering Algorithm. In Advances in Artificial Intelligence, Big Data and Algorithms (pp. 438-443). IOS Press.
Tie, Y., Wang, T., Jin, C., Li, X., & Yang, P. (2022). Wireless Communications and Mobile Computing Multitrack Music Generation Network Based on Music Rules. Wireless Communications and Mobile Computing, 2022(1), 5845689.
Tjahyanto, A., Suprapto, Y. K., Purnomo, M. H., & Wulandari, D. P. (2012, May). Fft-based features selection for javanese music note and instrument identification using support vector machines. In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE) (Vol. 1, pp. 439-443). IEEE.
Tomaz Neves, P. L., Fornari, J., & Batista Florindo, J. (2022). Self-attention generative adversarial networks applied to conditional music generation. Multimedia Tools and Applications, 81(17), 24419-24430.
Vall, A., Skowron, M., Knees, P., & Schedl, M. (2015). Improving Music Recommendations with a Weighted Factorization of the Tagging Activity. In ISMIR (pp. 65-71).
Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. Advances in neural information processing systems, 26.
Vemula, D.R., Tripathi, S.K., Sharma, N.K., Hussain, M.M., Swamy, U.R., Polavarapu, B.L. (2023). Music Generation Using Deep Learning. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore
Venkatachalam, N. (2024, January). MusiciAI: A Hybrid Generative Model for Music Therapy using Cross-Modal Transformer and Variational Autoencoder. In 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (pp. 1176-1180). IEEE.
Verma, J. P., Bhattacharya, P., Rathor, A. S., Shah, J., & Tanwar, S. (2022, July). Collaborative filtering-based music recommendation in view of negative feedback system. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021 (pp. 447-460). Singapore: Springer Nature Singapore.
Wahbi, I. S., Bahbouh, H. T., & Yahia, K. M. (2023, March). Transcription of Arabic and Turkish Music Using Convolutional Neural Networks. In 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) (Vol. 5, pp. 1-5). IEEE.
Wang, C., & Geng, X. (2023, June). Simulation of music style classification model based on support vector machine algorithm. In Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023) (Vol. 12703, pp. 266-272). SPIE.
Wang, H., Han, S., Li, G., & Zhao, B. (2022, October). A Hybrid Neural Network for Music Generation Using Frequency Domain Data. In International Conference on Artificial Intelligence in China (pp. 219-228). Singapore: Springer Nature Singapore.
Wang, H., Hao, S., Zhang, C., Wang, X., & Chen, Y. (2023). Motif transformer: Generating music with motifs. IEEE Access, 11, 63197-63204.
Wang, J. (2023). Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning. Applied Mathematics and Nonlinear Sciences.
Wang, L., Zhu, H., Zhang, X., Li, S., & Li, W. (2020). Transfer learning for music classification and regression tasks using artist tags. In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers (pp. 81-89). Springer Singapore.
Wang, S., Xu, C., Ding, A. S., & Tang, Z. (2021). A novel emotion-aware hybrid music recommendation method using deep neural network. Electronics, 10(15), 1769.
Wang, T., Liu, J., Jin, C., Li, J., & Ma, S. (2020, October). An intelligent music generation based on Variational Autoencoder. In 2020 International Conference on Culture-oriented Science & Technology (ICCST) (pp. 394-398). IEEE.
Wang, W., Li, J., Li, Y., & Xing, X. (2024). Style-conditioned music generation with Transformer-GANs. Frontiers of Information Technology & Electronic Engineering, 25(1), 106-120.
Wang, X. (2020). Research on recognition and classification of folk music based on feature extraction algorithm. Informatica, 44(4).
Wang, X., & Wang, Y. (2014, November). Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 627-636).
Wang, X., Jin, C., & Zhao, W. (2018). Beijing opera synthesis based on straight algorithm and deep learning. Advances in Multimedia, 2018(1), 5158164.
Wang, Y. (2023). Piano automatic transcription based on transformer. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-8.
Wang, Y. A., Huang, Y. K., Lin, T. C., Su, S. Y., & Chen, Y. N. (2019, May). Modeling melodic feature dependency with modularized variational auto-encoder. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 191-195). IEEE.
Wang, Z., Wang, D., Zhang, Y., & Xia, G. (2020). Learning interpretable representation for controllable polyphonic music generation. Proceedings of the 21st International Society for Music Information Retrieval Conference, ISMIR 2020, pp. 795–802
Wei, X., Chen, J., Zheng, Z., Guo, L., Li, L., & Wang, D. (2023). A Multi-Scale Attentive Transformer for Multi-Instrument Symbolic Music Generation. arXiv preprint arXiv:2305.16592.
Weiß, C., & Peeters, G. (2022). Comparing deep models and evaluation strategies for multi-pitch estimation in music recordings. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2814-2827.
Wiafe, A., Nutrokpor, C., Owusu, E., Kastriku, F. A., & Wiafe, I. (2022). Using genetic algorithms for music composition: implications of early termination on aesthetic quality. International Journal of Information Technology, 14(4), 1875-1881.
Williams, D., Hodge, V. J., Gega, L., Murphy, D., Cowling, P. I., & Drachen, A. (2019, March). AI and automatic music generation for mindfulness. In 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience. York.
Wu, C. (2024). Music Therapy Music Selection Based on Big Data Analysis. Applied Mathematics and Nonlinear Sciences, 9(1). Wu, G., Liu, S., & Fan, X. (2023). The power of fragmentation: a hierarchical transformer model for structural segmentation in symbolic music generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1409-1420.
Wu, H., & Zhu, L. (2024). Adaptive classification method of electronic music based on improved decision tree. International Journal of Arts and Technology, 15(1), 1-12.
Wu, S. L., & Yang, Y. H. (2023). MuseMorphose: Full-song and fine-grained piano music style transfer with one transformer VAE. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1953-1967.
Wu, S. L., & Yang, Y. H. (2023, June). Compose & Embellish: Well-structured piano performance generation via a two-stage approach. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Wu, Y. T., Chen, B., & Su, L. (2020). Multi-instrument automatic music transcription with self-attention-based instance segmentation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2796-2809.
Xia, J. (2022). Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing. International journal of grid and utility computing, 13(2-3), 183-194.
Xiang, M. (2022, November). Music Vocal Spectrum Classification Model based on Improved SVM Algorithm. In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1244-1247). IEEE.
Xiao, Z., Chen, X., & Zhou, L. (2023). Polyphonic piano transcription based on graph convolutional network. Signal Processing, 212, 109134.
Xin, Y. (2024). MusicEmo: transformer-based intelligent approach towards music emotion generation and recognition. Journal of Ambient Intelligence and Humanized Computing, 1-11.
Xu, L., Chen, Y., & Zhang, L. (2023, August). Intelligent Composition Based on Fibonacci Sequence and Hidden Markov Model. In Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology (pp. 1-6).
Xu, W., McAuley, J., Dubnov, S., & Dong, H. W. (2023, December). Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4512-4517). IEEE.
Xu, Y. (2024). Enhancing Music Generation With a Semantic-Based Sequence-to-Music Transformer Framework. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-19.
Xu, Y., Wang, W., Cui, H., Xu, M., & Li, M. (2022). Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy. EURASIP Journal on Audio, Speech, and Music Processing, 2022(1), 8.
Yadav, N., Kumar Singh, A., & Pal, S. (2022). Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system. Computational Intelligence, 38(4), 1232-1257.
Yang, R., Chen, T., Zhang, Y., & Xia, G. (2019). Inspecting and interacting with meaningful music representations using VAE. Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 307–312 Yang, X., Yu, Y., & Wu, X. (2022). Double linear transformer for background music generation from videos. Applied Sciences, 12(10), 5050.
Ycart, A., & Benetos, E. (2020). Learning and evaluation methodologies for polyphonic music sequence prediction with LSTMs. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1328-1341.
Ycart, A., McLeod, A., Benetos, E., & Yoshii, K. (2019). Blending acoustic and language model predictions for automatic music transcription.
Yi, J., Zhu, Y., Xie, J., & Chen, Z. (2021). Cross-modal variational auto-encoder for content-based micro-video background music recommendation. IEEE Transactions on Multimedia, 25, 515-528.
Yi, Y., Zhu, X., Yue, Y., & Wang, W. (2021, April). Music genre classification with LSTM based on time and frequency domain features. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. 678-682). IEEE.
Yi, Y., Zhu, X., Yue, Y., & Wang, W. (2021, April). Music genre classification with LSTM based on time and frequency domain features. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. 678-682). IEEE.
Yiiksel, A. Ç., Karci, M. M., & Uyar, A. Ş. (2011, June). Automatic music generation using evolutionary algorithms and neural networks. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 354-358). IEEE.
Yin, C. (2024). Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 19(1), 1-18.
Yin, Z., Reuben, F., Stepney, S., & Collins, T. (2023). Deep learning’s shallow gains: A comparative evaluation of algorithms for automatic music generation. Machine Learning, 112(5), 1785-1822.
Yingjun Gong; (2021). Application of virtual reality teaching method and artificial intelligence technology in digital media art creation. Ecological Informatics.
Yu, B., Lu, P., Wang, R., Hu, W., Tan, X., Ye, W., ... & Liu, T. Y. (2022). Museformer: Transformer with fine-and coarse-grained attention for music generation. Advances in Neural Information Processing Systems, 35, 1376-1388.
Yu, D., Duan, H., Fang, J., & Zeng, B. (2020). Predominant instrument recognition based on deep neural network with auxiliary classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 852-861.
Yuan, Y., & Liu, J. (2024). Music Classification and Identification Based on Convolutional Neural Network, Computer-Aided Design and Applications , 21(S18), pp. 205–221
Zehren, M., Alunno, M., & Bientinesi, P. (2023). High-quality and reproducible automatic drum transcription from crowdsourced data. Signals, 4(4), 768-787.
Zhang, J. (2021). Music feature extraction and classification algorithm based on deep learning. Scientific Programming, 2021(1), 1651560.
Zhang, N. (2023). Learning adversarial transformer for symbolic music generation. IEEE transactions on neural networks and learning systems, 34(4), 1754-1763.
Zhang, R., Bai, J., Guan, X., Li, Q. (2021). Music Source Separation Method Based on Unet Combining SE and BiSRU. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 49(11).
Zhang, T. (2024). Application of Unet-SE-Bisru Algorithm for Music Signal Processing in Music Source Separation. Scalable Computing: Practice and Experience, 25(4), 2960-2972.
Zhang, T., & Liu, S. (2022). [Retracted] Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph. Security and Communication Networks, 2022(1), 5889724.
Zhang, Y., Lv, X., Li, Q., Wu, X., Su, Y., & Yang, H. (2024). An automatic music generation method based on RSCLN_Transformer network. Multimedia Systems, 30(1), 4.
Zhang, Z., Yu, Y., & Takasu, A. (2023). Controllable lyrics-to-melody generation. Neural Computing and Applications, 35(27), 19805-19819.
Zhao, W., & Yin, B. (2022, January). Environmental sound classification based on pitch shifting. In 2022 International Seminar on Computer Science and Engineering Technology (SCSET) (pp. 275-280). IEEE.
Zhao, W., Zhou, Y., Tie, Y., & Zhao, Y. (2018, October). Recurrent neural network for MIDI music emotion classification. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 2596-2600). IEEE.
Zheng, H. T., Chen, J. Y., Liang, N., Sangaiah, A. K., Jiang, Y., & Zhao, C. Z. (2019). A deep temporal neural music recommendation model utilizing music and user metadata. Applied Sciences, 9(4), 703.
Zheng, L., & Li, C. (2024). Real-Time Emotion-Based Piano Music Generation using Generative Adversarial Network (GAN). IEEE Access.
Zheng, R., Liu, X., & Butala, M. D. (2023, January). Multi-Instrument Polyphonic Melody Transcription Based on Deep Learning. In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 762-767). IEEE.
Zhou, J., Niu, T., Zhu, H., & Wang, X. (2023). CoCoFormer: A controllable feature-rich polyphonic music generation method. In Summit on Music Intelligence (pp. 95-107). Singapore: Springer Nature Singapore.
Zhu, Q., Shankar, A., & Maple, C. (2024). Grey wolf optimizer based deep learning mechanism for music composition with data analysis. Applied Soft Computing, 153, 111294.
Zhu, Y., Olszewski, K., Wu, Y., Achlioptas, P., Chai, M., Yan, Y., & Tulyakov, S. (2022, October). Quantized gan for complex music generation from dance videos. In European Conference on Computer Vision (pp. 182-199). Cham: Springer Nature Switzerland.
Ziegler, Z., & Rush, A. (2019, May). Latent normalizing flows for discrete sequences. In International Conference on Machine Learning (pp. 7673-7682). PMLR.
Zou, Y., Zou, P., Zhao, Y., Zhang, K., Zhang, R., & Wang, X. (2022, May). Melons: generating melody with long-term structure using transformers and structure graph. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 191-195). IEEE.
Zulfikar, W. B., Gerhana, Y. A., Almi, A. Y. P., Maylawati, D. S. A., & Al Amin, M. I. (2023, November). Mood of Song Detection Using Mel Frequency Cepstral Coefficient and Convolutional Neural Network with Tuning Hyperparameter. In 2023 11th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-6). IEEE.
اخشابی مجید، درتاج فریبرز. (1401). شناسایی متغیرهای میانجی در رابطه میان آموزش موسیقی و افزایش خلاقیت. فصلنامه علمی پژوهشی علوم روانشناختی، شماره ۲۱ ،۸۶۶-۸۵۳.
استخریان, امیر رضا, آزرم, الهام. (1400). مرور سیستماتیک مطالعات مرتبط با هوش مصنوعی و اینترنت اشیاء در شرکت های کوچک و متوسط(SME). اولین کنفرانس بین المللی برق و کامپیوتر، مکانیک و هوش مصنوعی.
بافنده, علی., شمول, داوود. (سال ششم، شماره 15، 1387). هوش مصنوعی و کاربرد آن در مدیریت. فروغ تدبیر, 44-49.
ترابی, علیرضا, احمدی زاد, آرمان, اصغر نیا, مرتضی (شماره 8، تابستان 1401). فرصتها و چالشها و روندهای جهانی فناوری تنظیم گری (مرور مبانی نظری). فصلنامه توسعه تکنولوژی صنعتی, 3-16.
حشمدار, اکرم, کردی, مراد. (بهار1401 ،سال چهارم، شماره 12). بررسی اثربخشی سیستم های هوش مصنوعی در کارکردهای منابع انسانی. فصلنامه پژوهش های معاصر در علوم مدیریت و حسابداری.
زیودار, زهره. (سال ششم، شماره 84، بهار 1401). کاربرد تکنیکهای هوش مصنوعی در حوزه حسابداری و مالی. فصلنامه رویکردهای پژوهشی نوین در مدیریت و حسابداری, 1572-1557.
کارکن بیرق, حبیب. (شماره 37، زمستان 1388). نگرش انتقادی به مسئه هوش مصنوعی. نشریه ذهن, 59-84.
کلاته رحمانی, راحله، چهارده چریکی, معصومه. (1389). هوش مصنوعی و کاربرد آن در حسابداری مالی. کنکاش, 135-140.
محمدعلی خلج, محمدحسین. (سال پنجم، شماره اول، بهار و تابستان 1393). درﻳﻔﻮس و ﺗﺎرﻳﺦ ﻓﻠﺴﻔﻲ ﻫﻮش ﻣﺼﻨﻮﻋﻲ. فصلنامه ﻏﺮب ﺷﻨﺎﺳﻲ بنیادی، پژوهشگاه علوم انسانی و مطالعات فرهنگی, 128-103.
نصیری مفخم، فریا. روغنی زاده، راضیه(1393).. ساخت هوشمند موسیقی با الگوریتم ژنتیک مبتنی بر جهش یکنواخت موتیف، فصلنامه هوش محاسباتی در مهندسی برق، 5(4)، 85-102.
Achar, B. A., Aiyappa, N. D., Akshaj, B., Thippeswamy, M. N., & Pillay, N. (2019). Activity-Based Music Classifier: A Supervised Machine Learning Approach for Curating Activity-Based Playlists. In Emerging Research in Computing, Information, Communication and Applications: ERCICA 2018, Volume 2 (pp. 185-198). Springer Singapore.
Adkins, S., Sarmento, P., & Barthet, M. (2023, April). LooperGP: a loopable sequence model for live coding performance using guitarpro tablature. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 3-19). Cham: Springer Nature Switzerland.
Akbari, M., Liang, J., & Cheng, H. (2018). A real-time system for online learning-based visual transcription of piano music. Multimedia tools and applications, 77, 25513-25535.
Araneda-Hernandez, M., Bravo-Marquez, F., Parra, D., & Cádiz, R. F. (2023). MUSIB: musical score inpainting benchmark. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 19.
Arnold, A. N., & Vairamuthu, S. (2019). Music recommendation using collaborative filtering and deep learning. Int. J. Innov. Technol. Explor. Eng.(IJITEE), 8(7), 964-968.
Arronte Alvarez, A., & Gómez, F. (2021). Motivic pattern classification of music audio signals combining residual and LSTM networks, International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), pp. 208–214.
Arronte Alvarez, A., & Gómez, F. (2021). Motivic pattern classification of music audio signals combining residual and LSTM networks, International Journal of Interactive Multimedia and Artificial Intelligence, 6(6), pp. 208–214.
Ashraf, M., Abid, F., Din, I. U., Rasheed, J., Yesiltepe, M., Yeo, S. F., & Ersoy, M. T. (2023). A hybrid cnn and rnn variant model for music classification. Applied Sciences, 13(3), 1476.
Ashraf, M., Geng, G., Wang, X., Ahmad, F., & Abid, F. (2020). A globally regularized joint neural architecture for music classification. IEEE Access, 8, 220980-220989.
Ayata, D., Yaslan, Y., & Kamasak, M. E. (2018). Emotion based music recommendation system using wearable physiological sensors. IEEE transactions on consumer electronics, 64(2), 196-203.
Bai, J., Luo, K., Peng, J., Shi, J., Wu, Y., Feng, L., ... & Wang, Y. (2017, July). Music emotions recognition by cognitive classification methodologies. In 2017 IEEE 16th International Conference on Cognitive Informatics & Cognitive Computing (ICCI* CC) (pp. 121-129). IEEE.
Bai, Y. (2024, January). A music generation model based on Bi-LSTM. In International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023) (Vol. 12969, pp. 430-435). SPIE.
Banerjee, S., Rath, M., Swain, T., & Samant, T. (2022, April). Music generation using time distributed dense stateful char-RNNs. In 2022 IEEE 7th International conference for Convergence in Technology (I2CT) (pp. 1-5). IEEE.
Barbancho, I., Tzanetakis, G., Barbancho, A. M., & Tardón, L. J. (2018). Discrimination between ascending/descending pitch arpeggios. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(11), 2194-2203.
Barnabò, G., Trappolini, G., Lastilla, L., Campagnano, C., Fan, A., Petroni, F., & Silvestri, F. (2023). Cycledrums: automatic drum arrangement for bass lines using cyclegan. Discover Artificial Intelligence, 3(1), 4.
Bayu, Q. D. A. P., Suyanto, S., & Arifianto, A. (2019, December). Hierarchical SVM-kNN to classify music emotion. In 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI) (pp. 5-10). IEEE.
Benetos, E., & Dixon, S. (2011). Joint multi-pitch detection using harmonic envelope estimation for polyphonic music transcription. IEEE Journal of Selected Topics in Signal Processing, 5(6), 1111-1123.
Benetos, E., & Dixon, S. (2012). A shift-invariant latent variable model for automatic music transcription. Computer Music Journal, 36(4), 81-94.
Benetos, E., & Weyde, T. (2013). Explicit duration hidden markov models for multiple-instrument polyphonic music transcription.
Bhattarai, B., & Lee, J. (2024). Korean Pansori Vocal Note Transcription Using Attention-Based Segmentation and Viterbi Decoding. Applied Sciences, 14(2), 492.
Bhattarai, B., Pandeya, Y. R., & Lee, J. (2020). Parallel stacked hourglass network for music source separation. IEEE Access, 8, 206016-206027.
Bian, W., Wang, J., Zhuang, B., Yang, J., Wang, S., & Xiao, J. (2019). Audio-based music classification with DenseNet and data augmentation. In PRICAI 2019: Trends in Artificial Intelligence: 16th Pacific Rim International Conference on Artificial Intelligence, Cuvu, Yanuca Island, Fiji, August 26-30, 2019, Proceedings, Part III 16 (pp. 56-65). Springer International Publishing.
Bihani, H., Bothe, S., Acharya, A., Desai, T., & Joglekar, P. (2023, April). Automatic Music Melody Generation Using LSTM and Markov Chain Model. In International Conference on Information and Communication Technology for Intelligent Systems (pp. 249-257). Singapore: Springer Nature Singapore.
Bodily, P., & Ventura, D. (2022). Steerable Music Generation which Satisfies Long-Range Dependency Constraints. Transactions of the International Society for Music Information Retrieval, 5(1), 71-87.
Bogdanov, D., & Boyer, H. (2011). How much metadata do we need in music recommendation?: A subjective evaluation using preference sets. In Klapuri A, Leider C, editors. 12th International Society for Music Information Retrieval Conference (ISMIR 2011); 2011 Oct 24-28; Miami, USA. Montréal: ISMIR; 2011. p. 97-102. International Society for Music Information Retrieval (ISMIR).
Bogdanov, D., Haro, M., Fuhrmann, F., Xambó, A., Gómez, E., & Herrera, P. (2013). Semantic audio content-based music recommendation and visualization based on user preference examples. Information Processing & Management, 49(1), 13-33.
Bressan, G. M., de Azevedo, B. C., & Lizzi, E. A. S. (2017). A decision tree approach for the musical genres classification. Applied Mathematics & Information Sciences, 11(6), 1703-1713.
Burlet, G., & Hindle, A. (2017). Isolated guitar transcription using a deep belief network. PeerJ Computer Science, 3, e109.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2016, October). Early handwritten music recognition with hidden markov models. In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 319-324). IEEE.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2017, November). Handwritten music recognition for mensural notation: Formulation, data and baseline results. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (Vol. 1, pp. 1081-1086). IEEE.
Calvo-Zaragoza, J., Toselli, A. H., & Vidal, E. (2019). Hybrid hidden Markov models and artificial neural networks for handwritten music recognition in mensural notation. Pattern Analysis and Applications, 22, 1573-1584.
Cañadas Quesada, F. J., Ruiz Reyes, N., Vera Candeas, P., Carabias, J. J., & Maldonado, S. (2010). A multiple-F0 estimation approach based on Gaussian spectral modelling for polyphonic music transcription. Journal of New Music Research, 39(1), 93-107.
Cao, B., Fukumori, T., & Yamashita, Y. (2023, October). Multi-Instruments Music Generation Based on Chord Input. In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) (pp. 1082-1083). IEEE.
Carthen, C. D., Le, V., Kelley, R., Kozubowski, T. J., & Harris Jr, F. C. (2017). Rewind: An Automatic Music Transcription Web Application. INTERNATIONAL JOURNAL OF COMPUTERS AND THEIR APPLICATIONS, 20.
Cazau, D., Revillon, G., Krywyk, J., & Adam, O. (2015). An investigation of prior knowledge in Automatic Music Transcription systems. The Journal of the Acoustical Society of America, 138(4), 2561-2573.
Cazau, D., Wang, Y., Adam, O., Wang, Q., & Nuel, G. (2017, October). Improving Note Segmentation in Automatic Piano Music Transcription Systems with a Two-State Pitch-Wise HMM Method. In ISMIR (pp. 523-530).
Cazau, D., Wang, Y., Chemillier, M., & Adam, O. (2016). An automatic music transcription system dedicated to the repertoires of the marovany zither. Journal of New Music Research, 45(4), 343-360.
Chakrabarty, S., Roy, S., & De, D. (2014). Pervasive diary in music rhythm education: a context-aware learning tool using genetic algorithm. In Advanced Computing, Networking and Informatics-Volume 1: Advanced Computing and Informatics Proceedings of the Second International Conference on Advanced Computing, Networking and Informatics (ICACNI-2014) (pp. 669-677). Cham: Springer International Publishing.
Chandna, P., Miron, M., Janer, J., & Gómez, E. (2017). Monoaural audio source separation using deep convolutional neural networks. In Latent Variable Analysis and Signal Separation: 13th International Conference, LVA/ICA 2017, Grenoble, France, February 21-23, 2017, Proceedings 13 (pp. 258-266). Springer International Publishing.
Chang, H. Y., Huang, S. C., & Wu, J. H. (2017). A personalized music recommendation system based on electroencephalography feedback. Multimedia Tools and Applications, 76, 19523-19542.
Chang, J. W., Chiou, C. Y., Liao, J. Y., Hung, Y. K., Huang, C. C., Lin, K. C., & Pu, Y. H. (2021). Music recommender using deep embedding-based features and behavior-based reinforcement learning. Multimedia Tools and Applications, 1-28.
Chaudhary, D., Singh, N. P., & Singh, S. (2021). Emotion-Based Hindi Music Classification. In Proceedings of International Conference on Artificial Intelligence and Applications: ICAIA 2020 (pp. 553-563). Springer Singapore.
Chavare, S. R., Awati, C. J., & Shirgave, S. K. (2021, January). Smart recommender system using deep learning. In 2021 6th international conference on inventive computation technologies (ICICT) (pp. 590-594). IEEE.
Chen, C., & Li, Q. (2020). A multimodal music emotion classification method based on multifeature combined network classifier. Mathematical Problems in Engineering, 2020(1), 4606027.
Chen, H. (2024). Music Transcription Based on Deep Learning. Proceedings of 2023 International Conference on New Trends in Computational Intelligence, NTCI 2023, pp. 62–65.
Chen, J., Liu, Y., & Li, D. (2016). Dynamic group recommendation with modified collaborative filtering and temporal factor. Int. Arab J. Inf. Technol., 13(2), 294-301.
Chen, L., Shen, L., Yu, D., Wang, Z., Qian, K., Hu, B., ... & Yamamoto, Y. (2023, October). Multi-Track Music Generation with WGAN-GP and Attention Mechanisms. In 2023 IEEE 12th Global Conference on Consumer Electronics (GCCE) (pp. 606-607). IEEE.
Chen, T. P., & Su, L. (2021). Attend to Chords: Improving Harmonic Analysis of Symbolic Music Using Transformer-Based Models. Trans. Int. Soc. Music. Inf. Retr., 4(1), 1-13.
Chen, Y., & Zheng, H. (2023). The application of HMM algorithm based music note feature recognition teaching in universities. Intelligent Systems with Applications, 20, 200277.
Cheng, R., & Tang, B. (2016). A music recommendation system based on acoustic features and user personalities. In Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2016 Workshops, BDM, MLSDA, PACC, WDMBF, Auckland, New Zealand, April 19, 2016, Revised Selected Papers 20 (pp. 203-213). Springer International Publishing.
Choi, H. S., Lee, J., & Lee, K. (2020). Spec2spec: Towards the general framework of music processing using generative adversarial networks. Acoustical Science and Technology, 41(1), 160-165.
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017). Transfer learning for music classification and regression tasks. arXiv preprint arXiv:1703.09179.
Choi, K., Fazekas, G., Sandler, M., & Cho, K. (2017, March). Convolutional recurrent neural networks for music classification. In 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP) (pp. 2392-2396). IEEE.
Choi, K., Hawthorne, C., Simon, I., Dinculescu, M., & Engel, J. (2020, November). Encoding musical style with transformer autoencoders. In International conference on machine learning (pp. 1899-1908). PMLR.
Comanducci, L., Gioiosa, D., Zanoni, M., Antonacci, F., & Sarti, A. (2023). Variational Autoencoders for chord sequence generation conditioned on Western harmonic music complexity. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 24.
Costa, Y. M., Oliveira, L. S., & Silla Jr, C. N. (2017). An evaluation of convolutional neural networks for music classification using spectrograms. Applied soft computing, 52, 28-38.
Cuesta, H., & Gómez Gutiérrez, E. (2022). Voice assignment in vocal quartets using deep learning models based on pitch salience. Transactions of the International Society for Music Information Retrieval. 2022; 5 (1): 99-112.
Dai, J. (2022, February). Intelligent Music Style Classification System Based on K-Nearest Neighbor Algorithm and Artificial Neural Network. In 2022 IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) (pp. 531-543). IEEE.
Dai, S., Jin, Z., Gomes, C., & Dannenberg, R. B. (2021). Controllable deep melody generation via hierarchical music structure
representation. Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021, pp. 143–150 Dai, Z., & Huang, X. (2022, September). Electronic Dance Music Classification Based on Machine Learning Methods. In 2022 International Conference on Electronics and Devices, Computational Science (ICEDCS) (pp. 351-354). IEEE.
Dalmazzo, D., Déguernel, K., & Sturm, B. L. (2024, March). The Chordinator: Modeling Music Harmony by Implementing Transformer Networks and Token Strategies. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 52-66). Cham: Springer Nature Switzerland.
Danyang, L., & Mingxin, G. (2021). Music Recommendation Method Based on Multi-Source Information Fusion. Data Analysis and Knowledge Discovery, 5(2), 94-105.
Darshna, P. (2018, January). Music recommendation based on content and collaborative approach & reducing cold start problem. In 2018 2nd international conference on inventive systems and control (ICISC) (pp. 1033-1037). IEEE.
De Boom, C., Van Laere, S., Verbelen, T., & Dhoedt, B. (2020). Rhythm, chord and melody generation for lead sheets using recurrent neural networks. In Machine Learning and Knowledge Discovery in Databases: International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part II (pp. 454-461). Springer International Publishing. de la Fuente, C., Valero-Mas, J. J., Castellanos, F. J., & Calvo-Zaragoza, J. (2022). Multimodal image and audio music transcription. International Journal of Multimedia Information Retrieval, 11(1), 77-84.
Deepak, S., & Prasad, B. G. (2020, July). Music Classification based on Genre using LSTM. In 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 985-991). IEEE.
Demirel, E., Ahlbäck, S., & Dixon, S. (2020, July). Automatic lyrics transcription using dilated convolutional neural networks with self-attention. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Deng, Y., Lv, Y., Liu, M., & Lu, Q. (2015, December). A regression approach to categorical music emotion recognition. In 2015 IEEE International Conference on Progress in Informatics and Computing (PIC) (pp. 257-261). IEEE.
Di, S., Jiang, Z., Liu, S., Wang, Z., Zhu, L., He, Z., ... & Yan, S. (2021, October). Video background music generation with controllable music transformer. In Proceedings of the 29th ACM International Conference on Multimedia (pp. 2037-2045).
Donahue, C., Mao, H. H., Li, Y. E., Cottrell, G. W., & McAuley, J. (2019). LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. arXiv preprint arXiv:1907.04868.
Dong, H. W., Chen, K., Dubnov, S., McAuley, J., & Berg-Kirkpatrick, T. (2023, June). Multitrack music transformer. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Downie, J. S. (2003). Music information retrieval. Annual review of information science and technology, 37(1), 295-340.
Dua, M., Yadav, R., Mamgai, D., & Brodiya, S. (2020). An improved RNN-LSTM based novel approach for sheet music generation. Procedia Computer Science, 171, 465-474.
Egivenia, E., Ryanie Setiawan, G., Shania Mintara, S., & Suhartono, D. (2021, September). Classification of explicit music content based on lyrics, music metadata, and user annotation. In Proceedings of the 6th International Conference on Sustainable Information Engineering and Technology (pp. 265-270).
Elbir, A., & Aydin, N. (2020). Music genre classification and music recommendation by using deep learning. Electronics Letters, 56(12), 627-629.
Evangeline, D., Bhuvan, M., Parkavi, A., Manikarnike, A., Harikrishnan, M., & Adiga, B. S. (2023, October). Automated Music Synthesis Using Recurrent Neural Network and Long Short Term Memory. In 2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (Vol. 1, pp. 1-5). IEEE.
Fan, Z. C., Lai, Y. L., & Jang, J. S. R. (2018, April). SVSGAN: singing voice separation via generative adversarial network. In 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 726-730). IEEE.
Fang, J. T., & Yang, C. W. (2022). SINGLE-CHANNEL MUSIC SOURCE SEPARATION BY HARMONIC STRUCTURE MODEL AND SUPPORT VECTOR MACHINE. 電機工程學刊, 29(2), 43-51.
Fang, Y., & Gui-fa, T. (2015). Visual music score detection with unsupervised feature learning method based on k-means. International Journal of Machine Learning and Cybernetics, 6, 277-287.
Farzaneh, M., & Mahdian Toroghi, R. (2020). Music generation using an interactive evolutionary algorithm. In Pattern Recognition and Artificial Intelligence: Third Mediterranean Conference, MedPRAI 2019, Istanbul, Turkey, December 22–23, 2019, Proceedings 3 (pp. 207-217). Springer International Publishing.
Ferano, F. C. A., Zahra, A., & Kusuma, G. P. (2023). Stacking ensemble learning for optical music recognition. Bulletin of Electrical Engineering and Informatics, 12(5), 3095-3104.
Fernando, R., Ranasinghe, P., Ranasinghe, U., Wijayakulasooriya, J., & Perera, P. (2023, September). Hybrid y-net architecture for singing voice separation. In 2023 31st European Signal Processing Conference (EUSIPCO) (pp. 156-160). IEEE.
Ferraro, A., Bogdanov, D., Yoon, J., Kim, K., & Serra, X. (2018). Automatic playlist continuation using a hybrid recommender system combining features from text and audio. In Proceedings of the ACM Recommender Systems Challenge 2018 (pp. 1-5).
Fessahaye, F., Perez, L., Zhan, T., Zhang, R., Fossier, C., Markarian, R., ... & Oh, P. (2019, January). T-recsys: A novel music recommendation system using deep learning. In 2019 IEEE international conference on consumer electronics (ICCE) (pp. 1-6). IEEE.
Ford, C., Noel-Hirst, A., Cardinale, S., Loth, J., Sarmento, P., Wilson, E., ... & Bryan-Kinns, N. (2024, June). Reflection Across AI-based Music Composition. In Proceedings of the 16th Conference on Creativity & Cognition (pp. 398-412).
Fu, Z., Lu, G., Ting, K. M., & Zhang, D. (2010, August). Learning naive Bayes classifiers for music classification and retrieval. In 2010 20th international conference on pattern recognition (pp. 4589-4592). IEEE.
Fujieda, M., Murakami, T., & Ishida, Y. (2011). An approach to multi-pitch estimation using a support vector machine. International Journal of Computers and Applications, 33(3), 202-210.
Gale, E., Matthews, O., Costello, B. D. L., & Adamatzky, A. (2013). Beyond markov chains, towards adaptive memristor
network-based music generation. International Journal of Unconventional Computing, 10(3), 181–197 Gan, C., Huang, D., Chen, P., Tenenbaum, J. B., & Torralba, A. (2020). Foley music: Learning to generate music from videos. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 (pp. 758-775). Springer International Publishing.
Gao, H. (2022). Automatic recommendation of online music tracks based on deep learning. Mathematical Problems in Engineering, 2022(1), 5936156.
Garanayak, M., Nayak, S. K., Sangeetha, K., Choudhury, T., & Shitharth, S. (2022). Content and Popularity-Based Music Recommendation System. International Journal of Information System Modeling and Design (IJISMD), 13(7), 1-14.
Garani, S. S., & Seshadri, H. (2019). An algorithmic approach to South Indian classical music. Journal of Mathematics and Music, 13(2), 107-134.
Gillick, J., Yang, J., Cella, C. E., & Bamman, D. (2021). Drumroll Please: Modeling Multi-Scale Rhythmic Gestures with Flexible Grids. Transactions of the International Society for Music Information Retrieval, 4(1), 156-167.
Goel, K., Vohra, R., & Sahoo, J. K. (2014). Polyphonic music generation by modeling temporal dependencies using a rnn-dbn. In Artificial Neural Networks and Machine Learning–ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings 24 (pp. 217-224). Springer International Publishing.
Goel, N., Srivastava, T., Yuvraj, Monika, & Koundal, D. (2022, December). A Piano Tune Generation Model Using Deep Learning. In International Conference on Soft Computing and Pattern Recognition (pp. 424-432). Cham: Springer Nature Switzerland.
Gómez de Silva Garza, A., & Herrera González, E. (2012). Music Style Analysis Using the Random Forest Algorithm. In DS 73-2 Proceedings of the 2nd International conference on Design Creativity Volume 2 (pp. 343-350).
Grekow, J. (2023). Generating Polyphonic Symbolic Emotional Music in the Style of Bach Using Convolutional Conditional Variational Autoencoder. IEEE Access, 11, pp. 93019–93031
Gupta, C., Li, H., & Goto, M. (2022). Deep learning approaches in topics of singing information processing. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2422-2451.
Gupta, K., Srivastava, K., Singh, K., & Pillai, R. (2023, July). Music Generation and Retrieval Using LSTM and VAE. In 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
Gupta, P., & Dwivedi, P. (2023, December). Advancements in Music Generation: Leveraging RNN and LSTM Networks for Automated Composition. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 434-439). IEEE.
Hadjeres, G., & Nielsen, F. (2020). Anticipation-RNN: Enforcing unary constraints in sequence generation, with application to interactive music generation. Neural Computing and Applications, 32(4), 995-1005.
Han, Y., Kim, J., & Lee, K. (2016). Deep convolutional neural networks for predominant instrument recognition in polyphonic music. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(1), 208-221.
Hariri, N., Mobasher, B., & Burke, R. (2012, November). Using social tags to infer context in hybrid music recommendation. In Proceedings of the twelfth international workshop on Web information and data management (pp. 41-48).
He, Q. (2022). A Music Genre Classification Method Based on Deep Learning. Mathematical Problems in Engineering, 2022(1), 9668018.
Hernandez-Olivan, C., Zay Pinilla, I., Hernandez-Lopez, C., & Beltran, J. R. (2021). A comparison of deep learning methods for timbre analysis in polyphonic automatic music transcription. Electronics, 10(7), 810.
Highams, L., & Olszewska, J. I. (2023, July). Markov-chain-based musical creative intelligent agent passing successfully the Turing Test. In 2023 IEEE 27th International Conference on Intelligent Engineering Systems (INES) (pp. 000215-000220). IEEE.
Hofmann, D. M. (2015). A genetic programming approach to generating musical compositions. In Evolutionary and Biologically Inspired Music, Sound, Art and Design: 4th International Conference, EvoMUSART 2015, Copenhagen, Denmark, April 8-10, 2015, Proceedings 4 (pp. 89-100). Springer International Publishing.
Hsiao, W. Y., Liu, J. Y., Yeh, Y. C., & Yang, Y. H. (2021, May). Compound word transformer: Learning to compose full-song music over dynamic directed hypergraphs. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 1, pp. 178-186).
Hsu, C., & Greer, R. (2023, December). Bridging Subjectivity and Objectivity in Evaluation of Machine-Generated Jazz Music: A Multimetric Approach. In 2023 IEEE International Symposium on Multimedia (ISM) (pp. 232-237). IEEE.
Hu, J., Ge, Z., & Wang, X. (2022). [Retracted] The Psychological Education Strategy of Music Generation and Creation by Generative Confrontation Network under Deep Learning. Computational Intelligence and Neuroscience, 2022(1), 3847415.
Hu, Z., Liu, Y., Chen, G., & Liu, Y. (2022). Can machines generate personalized music? a hybrid favorite-aware method for user preference music transfer. IEEE Transactions on Multimedia, 25, 2296-2308.
Hu, Z., Ma, X., Liu, Y., Chen, G., Liu, Y., & Dannenberg, R. B. (2023). The beauty of repetition: an algorithmic composition model with motif-level repetition generator and outline-to-music generator in symbolic music generation. IEEE Transactions on Multimedia.
Huang, J., Huang, X., Yang, L., & Tao, Z. (2024). Dance-Conditioned Artistic Music Generation by Creative-GAN. IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, 107(5), 836-844.
Huang, W., Xue, Y., Xu, Z., Peng, G., & Wu, Y. (2022). Polyphonic music generation generative adversarial network with Markov decision process. Multimedia Tools and Applications, 81(21), 29865-29885.
Huang, W., Yu, Y., Xu, H., Su, Z., & Wu, Y. (2023). Hyperbolic music transformer for structured music generation. IEEE Access, 11, 26893-26905.
Hulme, E., Marshall, D., Sidorov, K., & Jones, A. (2024, June). Acoustic Classification of Guitar Tunings with Deep Learning. In Proceedings of the 11th International Conference on Digital Libraries for Musicology (pp. 6-14).
Hung, Y. K., Pei, Y., & Li, J. (2023, July). Single-to-Multi Music Track Composition Using Interactive Chaotic Evolution. In International Conference on Frontier Computing (pp. 211-221). Singapore: Springer Nature Singapore.
Hwang, S. R., & Park, Y. C. (2020). Chord-based stepwise Korean Trot music generation technique using RNN-GAN. The Journal of the Acoustical Society of Korea, 39(6), 622-628.
Imasato, N., Miyazawa, K., Duncan, C., & Nagai, T. (2023). Using a language model to generate music in its symbolic domain while controlling its perceived emotion. IEEE Access, 11, 52412-52428.
Jain, N., Sah, N. K., Hawari, J., & Khare, S. (2024, April). Music Recommendation System based on Big Data using Pyspark. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-7). IEEE.
Jakubik, J. (2018). Evaluation of gated recurrent neural networks in music classification tasks. In Information Systems Architecture and Technology: Proceedings of 38th International Conference on Information Systems Architecture and Technology–ISAT 2017: Part I (pp. 27-37). Springer International Publishing.
Jaques, N., Gu, S., Turner, R. E., & Eck, D. (2017). Tuning recurrent neural networks with reinforcement learning. 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings.
Jeyalakshmi, C., Murugeshwari, B., & Karthick, M. (2018, August). HMM and K-NN based automatic musical instrument recognition. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 2018 2nd International Conference on (pp. 350-355). IEEE.
Ji, S., & Yang, X. (2023). Emomusictv: Emotion-conditioned symbolic music generation with hierarchical transformer vae. IEEE Transactions on Multimedia, 26, 1076-1088.
Ji, S., Yang, X., Luo, J., & Li, J. (2023). Rl-chord: Clstm-based melody harmonization using deep reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems.
Jiang, J., Xia, G. G., Carlton, D. B., Anderson, C. N., & Miyakawa, R. H. (2020, May). Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 516-520). IEEE.
Jiang, J., Xia, G. G., Carlton, D. B., Anderson, C. N., & Miyakawa, R. H. (2020, May). Transformer vae: A hierarchical model for structure-aware and interpretable music representation learning. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 516-520). IEEE.
Jin, C., Wang, T., Li, X., Tie, C. J. J., Tie, Y., Liu, S., ... & Huang, S. (2022). A transformer generative adversarial network for multi‐track music generation. CAAI Transactions on Intelligence Technology, 7(3), 369-380.
Jin, C., Wu, F., Wang, J., Liu, Y., Guan, Z., & Han, Z. (2022). MetaMGC: a music generation framework for concerts in metaverse. EURASIP journal on audio, speech, and music processing, 2022(1), 31.
Jing, L. (2024). Evolutionary Deep Learning for Sequential Data Processing in Music Education. Informatica, 48(8).
Jinming, Y. U., Zhuang, C. H. E. N., & Han, H. A. I. (2023). Multi-style Chord Music Generation Based on Artificial Neural Network. Journal of Donghua University (English Edition), 40(4).
Joel, J. S., Thompson, B. E., Thomas, S. R., Kumar, T. R., Prince, S., & Bini, D. (2023, April). Emotion based Music Recommendation System using Deep Learning Model. In 2023 International Conference on Inventive Computation Technologies (ICICT) (pp. 227-232). IEEE.
Johanson, B., Poli, R. (1998). GP-Music: An Interactive Genetic Programming System for Music Generation with Automated Fitness Raters”, Genetic Prog. Proc. of the Third Annual Conf. CSRP.
Johnson, D., & Tzanetakis, G. (2015, August). Guitar model recognition from single instrument audio recordings. In 2015 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM) (pp. 370-375). IEEE.
Joshi, S., Jain, T., & Nair, N. (2021, July). Emotion based music recommendation system using LSTM-CNN architecture. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 01-06). IEEE.
Kadandale, V. S., Montesinos, J. F., Haro, G., & Gómez, E. (2020, September). Multi-channel u-net for music source separation. In 2020 IEEE 22nd international workshop on multimedia signal processing (MMSP) (pp. 1-6). IEEE.
Kaliakatsos-Papakostas, M. A., Floros, A., & Vrahatis, M. N. (2016). Interactive music composition driven by feature evolution. SpringerPlus, 5, 1-38.
Kang, J., Poria, S., & Herremans, D. (2024). Video2music: Suitable music generation from videos using an affective multimodal transformer model. Expert Systems with Applications, 249, 123640.
Kang, S. I., & Lee, S. (2018). Improvement of speech/music classification for 3GPP EVS Based on LSTM. Symmetry, 10(11), 605.
Kasif, A., Sevgen, S., Ozcan, A., & Catal, C. (2024). Hierarchical multi-head attention LSTM for polyphonic symbolic melody generation. Multimedia Tools and Applications, 83(10), 30297-30317.
Kaushik, P., Rathore, S. P. S., Chahal, K., Saraf, S., Chauhan, G. S., & Kumar, P. (2024, March). RhythmQuest: Unifying Indian Music Classification and Prediction with Hybrid Deep Learning Techniques. In 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) (Vol. 2, pp. 1-6). IEEE.
Keerti, G., Vaishnavi, A. N., Mukherjee, P., Vidya, A. S., Sreenithya, G. S., & Nayab, D. (2022). Attentional networks for music generation. Multimedia Tools and Applications, 81(4), 5179-5189.
Khine, K. K., & Su, C. (2024, May). Acoustic Scene Classification Using Deep C-RNN Based on Log Mel Spectrogram and Gammatone Frequency Cepstral Coefficients Features. In 2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) (pp. 1-6). IEEE.
Khonglah, B. K., & Prasanna, S. M. (2015, December). Speech/music classification using vocal tract constriction aspect of speech. In 2015 Annual IEEE India Conference (INDICON) (pp. 1-6). IEEE.
Kim, J., & Kang, H. G. (2023). Contrastive learning based deep latent masking for music source separation. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2023-August, pp. 3709–3713.
Kim, J., Kim, K., You, K. H., & Lee, J. H. (2012). An approach for music recommendation using content-based analysis and collaborative filtering. International Information Institute (Tokyo). Information, 15(5), 1985.
Kolozali, Ş., Barthet, M., Fazekas, G., & Sandler, M. (2013). Automatic ontology generation for musical instruments based on audio analysis. IEEE transactions on audio, speech, and language processing, 21(10), 2207-2220.
Kong, M., Fan, L., Xu, S., Li, X., Hou, M., & Cao, C. (2024, April). Collaborative Filtering in Latent Space: A Bayesian Approach for Cold-Start Music Recommendation. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 105-117). Singapore: Springer Nature Singapore.
Kopel, M., Antczak, D., & Walczyński, M. (2023, July). Generating Music for Video Games with Real-Time Adaptation to Gameplay Pace. In Asian Conference on Intelligent Information and Database Systems (pp. 261-272). Singapore: Springer Nature Singapore.
Kozlov, D., Bakulin, M., Pavlov, S., Zuev, A., Krylova, M., & Kharchikov, I. (2023, June). Learning Properties of Holomorphic Neural Networks of Dual Variables. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Krause, M., Weiß, C., & Müller, M. (2023, June). Soft dynamic time warping for multi-pitch estimation and beyond. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Krishna, A. S., Rajkumar, P. V., Saishankar, K. P., & John, M. (2011, January). Identification of carnatic raagas using hidden markov models. In 2011 IEEE 9th International Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 107-110). IEEE.
Krishna, B. S., Chiluvuri, S. S. R. S., Kosuri, S. S. H., & Chimbili, S. S. H. (2023, December). The Application of Long Short-Term Memory and Transformers for Music Generation. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4475-4478). IEEE.
Kumar, C., Kumar, M., & Jindal, A. (2023, May). Session-based song recommendation using recurrent neural network. In Machine Intelligence Techniques for Data Analysis and Signal Processing: Proceedings of the 4th International Conference MISP 2022, Volume 1 (pp. 719-728). Singapore: Springer Nature Singapore.
Kumar, R., Luo, Y., & Mesgarani, N. (2018, September). Music Source Activity Detection and Separation Using Deep Attractor Network. In Interspeech (pp. 347-351).
Kumar, S., Gudiseva, K., Iswarya, A., Rani, S., Prasad, K., & Sharma, Y. K. (2022, October). Automatic music generation system based on RNN architecture. In 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS) (pp. 294-300). IEEE.
Kumble, A., Medatati, S., & Bhatt, A. (2023, February). Emotion-Based Music Recommendation. In International Conference on Advanced Computational and Communication Paradigms (pp. 147-155). Singapore: Springer Nature Singapore.
Kurniawan, F. (2011, July). Automatic music classification for Dangdut and campursari using Naïve Bayes. In Proceedings of the 2011 International Conference on Electrical Engineering and Informatics (pp. 1-6). IEEE.
Kurniawati, A., Yuniarno, E. M., Suprapto, Y. K., & Ikhsan Soewidiatmaka, A. N. (2023). Automatic note generator for Javanese gamelan music accompaniment using deep learning. International Journal of Advances in Intelligent Informatics, 9(2).
Kurniawati, A., Yuniarno, E. M., Suprapto, Y. K., & Ikhsan Soewidiatmaka, A. N. (2023). Automatic note generator for Javanese gamelan music accompaniment using deep learning. International Journal of Advances in Intelligent Informatics, 9(2).
Kwon, T., Jeong, D., & Nam, J. (2017). Audio-to-score alignment of piano music using RNN-based automatic music transcription. arXiv preprint arXiv:1711.04480.
Kwon, T., Jeong, D., & Nam, J. (2017). Audio-to-score alignment of piano music using RNN-based automatic music transcription. arXiv preprint arXiv:1711.04480.
Lazar, N., & Chithra, K. (2021). Comprehensive bibliometric mapping of publication trends in the development of Building Sustainability Assessment Systems. Environment, Development and Sustainability, 23(4), 4899–4923.
Lee, C. T., Yang, Y. H., & Chen, H. H. (2012). Multipitch estimation of piano music by exemplar-based sparse representation. IEEE Transactions on Multimedia, 14(3), 608-618.
Lee, D. H., Park, J. W., & Seo, Y. S. (2018). Multiple Regression-Based Music Emotion Classification Technique. KIPS Transactions on Software and Data Engineering, 7(6), 239-248.
Lee, J. H., Choi, H. S., & Lee, K. (2019). Audio query-based music source separation. Proceedings of the 20th International Society for Music Information Retrieval Conference, ISMIR 2019, pp. 878–885
Lee, J. H., Kim, J. Y., Jeong, D. K., & Kim, H. G. (2022). Music classification system through emotion recognition based on regression model of music signal and electroencephalogram features. The Journal of the Acoustical Society of Korea, 41(2), 115-121.
Li, C., & Zuo, X. (2024). Classical music recommendation algorithm on art market audience expansion under deep learning. Journal of Intelligent Systems, 33(1), 20230351.
Li, F. (2024). Chord-based music generation using long short-term memory neural networks in the context of artificial intelligence. The Journal of Supercomputing, 80(5), 6068-6092.
Li, G., Ding, S., & Li, Y. (2021, October). Novel LSTM-gan based music generation. In 2021 13th International Conference on Wireless Communications and Signal Processing (WCSP) (pp. 1-6). IEEE.
Li, J., Han, L., Wang, X., Wang, Y., Xia, J., Yang, Y., ... & Yan, H. (2024). A hybrid neural network model based on optimized margin softmax loss function for music classification. Multimedia Tools and Applications, 83(15), 43871-43906.
Li, P., Liang, T. M., Cao, Y. M., Wang, X. M., Wu, X. J., & Lei, L. Y. (2024). A novel Xi’an drum music generation method based on Bi-LSTM deep reinforcement learning. Applied Intelligence, 54(1), 80-94.
Li, P., Song, T., & Hu, J. (2023, August). Audio classification based on audio WSOLA and CNN algorithm. In Second International Conference on Electronic Information Technology (EIT 2023) (Vol. 12719, pp. 1145-1149). SPIE.
Li, P., Wu, J., & Ji, Z. (2023). HRPE: Hierarchical Relative Positional Encoding for Transformer-Based Structured Symbolic Music Generation. In Summit on Music Intelligence (pp. 122-134). Singapore: Springer Nature Singapore.
Li, S., & Sung, Y. (2021). INCO-GAN: variable-length music generation method based on inception model-based conditional GAN. Mathematics, 9(4), 387.
Li, S., & Sung, Y. (2023). MelodyDiffusion: chord-conditioned melody generation using a transformer-based diffusion model. Mathematics, 11(8), 1915.
Li, S., & Sung, Y. (2023). Transformer-based seq2seq model for chord progression generation. Mathematics, 11(5), 1111.
Li, T., Chen, J., Hou, H., & Li, M. (2021, January). Sams-net: A sliced attention-based neural network for music source separation. In 2021 12th International Symposium on Chinese Spoken Language Processing (ISCSLP) (pp. 1-5). IEEE.
Li, X., & Niu, Y. (2022). [Retracted] Research on Chord‐Constrained Two‐Track Music Generation Based on Improved GAN Networks. Scientific Programming, 2022(1), 5882468.
Li, X., Wang, K., Soraghan, J., & Ren, J. (2020). Fusion of Hilbert-Huang transform and deep convolutional neural network for predominant musical instruments recognition. In Artificial Intelligence in Music, Sound, Art and Design: 9th International Conference, EvoMUSART 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15–17, 2020, Proceedings 9 (pp. 80-89). Springer International Publishing.
Li, X., Yan, Y., Soraghan, J., Wang, Z., & Ren, J. (2023). A music cognition–guided framework for multi-pitch estimation. Cognitive computation, 15(1), 23-35.
Li, Y., & Liu, X. (2022). An Intelligent Music Production Technology Based on Generation Confrontation Mechanism. Computational Intelligence and Neuroscience, 2022(1), 5083146.
Li, Y., Li, X., Lou, Z., & Chen, C. (2022). Long short-term memory-based music analysis system for music therapy. Frontiers in Psychology, 13, 928048.
Li, Z., Huang, Q., Yang, X., Chen, Q., & Zhang, L. (2024). Automatic Composition System Based on Transformer-XL. Applied Sciences, 14(13), 5765.
Li, Z., Peng, H., Tan, S., & Zhu, F. (2023, September). Music classification with convolutional and artificial neural network. In Journal of Physics: Conference Series (Vol. 2580, No. 1, p. 012059). IOP Publishing.
Liang, Q., & Wang, Y. (2024). Drawlody: Sketch-Based Melody Creation with Enhanced Usability and Interpretability. IEEE Transactions on Multimedia.
Liao, X., Li, X., Xu, Q., Wu, H., & Wang, Y. (2020). Improving ant collaborative filtering on sparsity via dimension reduction. Applied Sciences, 10(20), 7245.
Liao, Y., Xu, H., & Xu, K. (2023, July). MusicGAIL: A Generative Adversarial Imitation Learning Approach for Music Generation. In CAAI International Conference on Artificial Intelligence (pp. 505-516). Singapore: Springer Nature Singapore.
Lim, C., & Chang, J. H. (2012). Enhancing support vector machine-based speech/music classification using conditional maximum a posteriori criterion. IET signal processing, 6(4), 335-340.
Lim, C., Lee, S. R., & Chang, J. H. (2012). Efficient implementation of an SVM-based speech/music classifier by enhancing temporal locality in support vector references. IEEE Transactions on Consumer Electronics, 58(3), 898-904.
Lim, Y. Q., Chan, C. S., & Loo, F. Y. (2020, July). Style-conditioned music generation. In 2020 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
Lin, C. Y., Wang, L. C., & Tsai, K. H. (2018). Hybrid real-time matrix factorization for implicit feedback recommendation systems. Ieee Access, 6, 21369-21380.
Linlin, P. (2022). Tchaikovsky music recommendation algorithm based on deep learning. Mobile Information Systems, 2022(1), 1265451.
Liu, H. (2023). A novel Internet of things-enabled intelligent piano musical identification system using support vector machine. Soft Computing, 27(24), 19125-19142.
Liu, H., & Zhao, C. (2024). A Deep Learning Algorithm for Music Information Retrieval Recommendation System, Computer-Aided Design and Applications, 21(S13), 1–16.
Liu, H., Xie, L., Wu, J., & Yang, G. (2020). Channel-wise subband input for better voice and accompaniment separation on high resolution music. arXiv preprint arXiv:2008.05216.
Liu, X. (2022). Research on Music Genre Recognition Based on Improved Naive Bayes Algorithm. Mobile Information Systems, 2022(1), 1909928.
Liu, Y. (2023). Optimization of Teaching Innovation Path of Music Education in Colleges and Universities Based on the Stochastic Process Model. Applied Mathematics and Nonlinear Sciences, 9(1).
Liu, Y., Jin, Z., Cai, L., Li, J. (2011). Singing annotation approach for solfege evaluation. Qinghua Daxue Xuebao/Journal of Tsinghua University, 51(12), pp. 1808–1813
Liutkus, A., Cıfka, O., Wu, S. L., Simsekli, U., Yang, Y. H., & Richard, G. (2021, July). Relative positional encoding for transformers with linear complexity. In International Conference on Machine Learning (pp. 7067-7079). PMLR.
Lluís, F., Pons, J., & Serra, X. (2018). End-to-end music source separation: Is it possible in the waveform domain?. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2019-September, pp. 4619–4623
Lopez-Rincon, O., Starostenko, O., & Lopez-Rincon, A. (2022). Algorithmic music generation by harmony recombination with genetic algorithm. Journal of Intelligent & Fuzzy Systems, 42(5), 4411-4423.
Lordelo, C., Benetos, E., Dixon, S., Ahlbäck, S., & Ohlsson, P. (2020). Adversarial unsupervised domain adaptation for harmonic-percussive source separation. IEEE Signal Processing Letters, 28, 81-85.
Lu, M., & Deng, F. (2021). [Retracted] Application of Collaborative Filtering and Data Mining Technology in Personalized National Music Recommendation and Teaching. Security and Communication Networks, 2021(1), 3140341.
Luo, J., Yang, X., Ji, S., & Li, J. (2020). MG-VAE: deep Chinese folk songs generation with specific regional styles. In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers (pp. 93-106). Springer Singapore.
Luo, Y., & Yu, J. (2023). Music source separation with band-split RNN. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1893-1901.
Luo, Y., Chen, Z., Hershey, J. R., Le Roux, J., & Mesgarani, N. (2017, March). Deep clustering and conventional networks for music separation: Stronger together. In 2017 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 61-65). IEEE.
Maccarini, F., Oudin, M., Giraud, M., & Levé, F. (2024, March). Co-creative orchestration of Angeles with layer scores and orchestration plans. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 228-245). Cham: Springer Nature Switzerland.
Magadum, H., Azad, H. K., & Patel, H. (2024). Music recommendation using dynamic feedback and content-based filtering. Multimedia Tools and Applications, 1-20.
Magron, P., & Févotte, C. (2022). Neural content-aware collaborative filtering for cold-start music recommendation. Data Mining and Knowledge Discovery, 36(5), 1971-2005.
Mahanta, S. K., Basisth, N. J., Halder, E., Khilji, A. F. U. R., & Pakray, P. (2023). Exploiting cepstral coefficients and CNN for efficient musical instrument classification. Evolving Systems, 1-13.
Mahardhika, F., Warnars, H. L. H. S., & Heryadi, Y. (2018, September). Indonesian's dangdut music classification based on audio features. In 2018 Indonesian association for pattern recognition international conference (INAPR) (pp. 99-103). IEEE. Majidi, M., & Toroghi, R. M. (2023). A combination of multi-objective genetic algorithm and deep learning for music harmony generation. Multimedia Tools and Applications, 82(2), 2419-2435.
Makris, D., Agres, K. R., & Herremans, D. (2021, July). Generating lead sheets with affect: A novel conditional seq2seq framework. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
Malheiro, R., Panda, R., Gomes, P., & Paiva, R. P. (2016). Emotionally-relevant features for classification and regression of music lyrics. IEEE Transactions on Affective Computing, 9(2), 240-254.
Manilow, E., Wichern, G., Seetharaman, P., & Le Roux, J. (2019, October). Cutting music source separation some Slakh: A dataset to study the impact of training data quality and quantity. In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (pp. 45-49). IEEE.
Mao, Y., Zhong, G., Wang, H., & Huang, K. (2022). Music-CRN: An efficient content-based music classification and recommendation network. Cognitive Computation, 14(6), 2306-2316.
Matić, D. (2010). A genetic algorithm for composing music. Yugoslav Journal of Operations Research, 20(1), 157-177. McLeod, A., Schramm, R., Steedman, M., & Benetos, E. (2017). Automatic transcription of polyphonic vocal music. Applied Sciences, 7(12), 1285.
Miao, B. C., Guo, W. A., & Wang, L. (2019). A polyphony music generation system based on latent features and a recurrent neural network. CAAI Trans Intell Syst, 14(1), 158-164.
Mimilakis, S. I., Drossos, K., Virtanen, T., & Schuller, G. (2017, September). A recurrent encoder-decoder approach with skip-filtering connections for monaural singing voice separation. In 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
Min, J., Liu, Z., Wang, L., Li, D., Zhang, M., & Huang, Y. (2022). Music generation system for adversarial training based on deep learning. Processes, 10(12), 2515.
Miron, M., Janer Mestres, J., & Gómez Gutiérrez, E. (2017). Generating data to train convolutional neural networks for classical music source separation. In Lokki T, Pätynen J, Välimäki V, editors. Proceedings of the 14th Sound and Music Computing Conference; 2017 Jul 5-8; Espoo, Finland. Aalto: Aalto University; 2017. p. 227-33.. Aalto University.
Miron, M., Janer Mestres, J., & Gómez Gutiérrez, E. (2017). Monaural score-informed source separation for classical music using convolutional neural networks. In Hu X, Cunningham SJ, Turnbull D, Duan Z. ISMIR 2017. 18th International Society for Music Information Retrieval Conference; 2017 Oct 23-27; Suzhou, China.[Canada]: ISMIR; 2017. p. 55-62.. International Society for Music Information Retrieval (ISMIR).
Mohan, G. B., Kumar, R. P., Korrayi, S., Harshitha, M., Chaithanya, B. S. S., Saiteja, K., & Rohan, G. V. (2024, February). Emotion-Based Music Recommendation System-A Deep Learning Approach. In 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) (pp. 1-7). IEEE.
Mohanty, M. N., Tripathy, V., & Pattanaik, R. K. (2023, June). Music Regeneration with RNN Architecture Using LSTM. In 2023 International Conference in Advances in Power, Signal, and Information Technology (APSIT) (pp. 538-542). IEEE.
Mou, L., Sun, Y., Tian, Y., Sun, Y., Liu, Y., Zhang, Z., ... & Jain, R. (2023, July). MemoMusic 3.0: considering context at music recommendation and combining music theory at music generation. In 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 296-301). IEEE.
Muhamed, A., Li, L., Shi, X., Yaddanapudi, S., Chi, W., Jackson, D., ... & Smola, A. J. (2021, May). Symbolic music generation with transformer-gans. In Proceedings of the AAAI conference on artificial intelligence (Vol. 35, No. 1, pp. 408-417).
Mukherjee, H., Dhar, A., Ghosh, M., Obaidullah, S. M., Santosh, K. C., Phadikar, S., & Roy, K. (2020). Music chord inversion shape identification with LSTM-RNN. Procedia Computer Science, 167, 607-615.
Mukhopadhyay, S., Kumar, A., Parashar, D., & Singh, M. (2024). Enhanced Music Recommendation Systems: A Comparative Study of Content-Based Filtering and K-Means Clustering Approaches. Revue d'Intelligence Artificielle, 38(1).
Musto, C., Semeraro, G., Lops, P., De Gemmis, M., & Narducci, F. (2012). Leveraging social media sources to generate personalized music playlists. In E-Commerce and Web Technologies: 13th International Conference, EC-Web 2012, Vienna, Austria, September 4-5, 2012. Proceedings 13 (pp. 112-123). Springer Berlin Heidelberg.
Mycka, J., Żychowski, A., & Mańdziuk, J. (2023). Toward human-level tonal and modal melody harmonizations. Journal of Computational Science, 67, 101963.
Mysore, G. J., Smaragdis, P., & Raj, B. (2010, September). Non-negative hidden Markov modeling of audio with application to source separation. In International Conference on Latent Variable Analysis and Signal Separation (pp. 140-148). Berlin, Heidelberg: Springer Berlin Heidelberg.
Nabizadeh, A. H., Jorge, A. M., Tang, S., & Yu, Y. (2016, July). Predicting user preference based on matrix factorization by exploiting music attributes. In Proceedings of the ninth international c* conference on computer science & software engineering (pp. 61-66).
Nakamura, E., Benetos, E., Yoshii, K., & Dixon, S. (2018, April). Towards complete polyphonic music transcription: Integrating multi-pitch detection and rhythm quantization. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 101-105). IEEE.
Nakamura, E., Cuvillier, P., Cont, A., Ono, N., & Sagayama, S. (2015, October). Autoregressive hidden semi-Markov model of symbolic music performance for score following. In 16th International Society for Music Information Retrieval Conference (ISMIR).
Nakamura, E., Itoyama, K., & Yoshii, K. (2016, August). Rhythm transcription of MIDI performances based on hierarchical Bayesian modelling of repetition and modification of musical note patterns. In 2016 24th European Signal Processing Conference (EUSIPCO) (pp. 1946-1950). IEEE.
Nakano, M., Le Roux, J., Kameoka, H., Nakamura, T., Ono, N., & Sagayama, S. (2011, October). Bayesian nonparametric spectrogram modeling based on infinite factorial infinite hidden Markov model. In 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (pp. 325-328). IEEE.
Narducci, F., De Gemmis, M., & Lops, P. (2015, September). A general architecture for an emotion-aware content-based recommender system. In Proceedings of the 3rd Workshop on Emotions and Personality in Personalized Systems 2015 (pp. 3-6).
Narkhede, N., Mathur, S., Bhaskar, A., & Kalla, M. (2024). Music genre classification and recognition using convolutional neural network. Multimedia Tools and Applications, 1-16.
Navarro-Caceres, M., Oliveira, H. G., Martins, P., & Cardoso, A. (2020). Integration of a music generator and a song lyrics generator to create Spanish popular songs. Journal of Ambient Intelligence and Humanized Computing, 11(11), 4421-4437.
Ning, Hui, Li, Qian,(2020),Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm, Complexity,6643888.
Niu, Y. (2022). Collaborative Filtering‐Based Music Recommendation in Spark Architecture. Mathematical Problems in Engineering, 2022(1), 9050872.
Niyazov, A., Mikhailova, E., & Egorova, O. (2021, May). Content-based music recommendation system. In 2021 29th Conference of Open Innovations Association (FRUCT) (pp. 274-279). IEEE.
Nuo, M., Han, X., & Zhang, Y. (2023, November). Contrastive learning-based music recommendation model. In International Conference on Neural Information Processing (pp. 370-382). Singapore: Springer Nature Singapore.
Ojima, Y., Nakamura, E., Itoyama, K., & Yoshii, K. (2018). Chord-aware automatic music transcription based on hierarchical Bayesian integration of acoustic and language models. APSIPA Transactions on Signal and Information Processing, 7, e14.
Omowonuola, V., Wilkerson, B., & Kher, S. (2022, October). Hybrid Context-Content Based Music Recommendation System. In Proceedings of the Future Technologies Conference (pp. 121-132). Cham: Springer International Publishing.
Oramas, S., Nieto, O., Sordo, M., & Serra, X. (2017, August). A deep multimodal approach for cold-start music recommendation. In Proceedings of the 2nd workshop on deep learning for recommender systems (pp. 32-37).
Pan, J., Yu, S., Zhang, Z., Hu, Z., & Wei, M. (2022). The Generation of Piano Music Using Deep Learning Aided by Robotic Technology. Computational Intelligence and Neuroscience, 2022(1), 8336616.
Pan, X., Song, B., Liu, C., & Zhang, H. (2019, July). Music detecting and recording system based on support vector machine. In 2019 International Conference on Communications, Information System and Computer Engineering (CISCE) (pp. 244-248). IEEE.
Papadopoulos G,Wiggins, G. (1999).AI Methods for Algorithmic Composition: A Survey, a Critical view and Future Prospects”, AISB Symposium on Musical Creativity, Edinburgh, UK.
Papadopoulos, A., Roy, P., & Pachet, F. (2016). Assisted lead sheet composition using flowcomposer. In Principles and Practice of Constraint Programming: 22nd International Conference, CP 2016, Toulouse, France, September 5-9, 2016, Proceedings 22 (pp. 769-785). Springer International Publishing.
Park, J., Choi, K., Oh, S., Kim, L., & Park, J. (2023). Note-level singing melody transcription with transformers. Intelligent Data Analysis, 27(6), 1853-1871.
Patel, J., Padaria, A. A., Mehta, A., Chokshi, A., Patel, J. D., & Kapdi, R. (2023). ConCollA-A Smart Emotion-based Music Recommendation System for Drivers. Scalable Computing: Practice and Experience, 24(4), 919-939.
Patil, S. S., Patil, S. H., Pawar, A. M., Shandilya, R., Kadam, A. K., Jadhav, R. B., & Bewoor, M. S. (2023, November). Music Generation Using RNN-LSTM with GRU. In 2023 International Conference on Integration of Computational Intelligent System (ICICIS) (pp. 1-5). IEEE.
Pavan, V., & Dhanalakshmi, R. (2024, May). Performance evaluation of music genre prediction system using convolutional neural network and random forest. In AIP Conference Proceedings (Vol. 2853, No. 1). AIP Publishing.
Pavan, V., Vickram, A. S., & Dhanalakshmi, R. (2024, May). Efficient prediction of music genre using support vector machine and decision tree. In AIP Conference Proceedings (Vol. 2853, No. 1). AIP Publishing.
Peng, W., Tang, Y., & Ouyang, Y. (2023, June). Design of Computer-Aided Music Generation Model Based on Artificial Intelligence Algorithm. In International Conference on Computational Finance and Business Analytics (pp. 229-237). Cham: Springer Nature Switzerland.
Pezzat-Morales, M., Perez-Meana, H., & Nakashika, T. (2023). Fast Jukebox: Accelerating Music Generation with Knowledge Distillation. Applied Sciences, 13(9), 5630.
Pulis, M., & Bajada, J. (2021, September). Siamese Neural Networks for Content-based Cold-Start Music Recommendation. In Proceedings of the 15th ACM Conference on Recommender Systems (pp. 719-723).
Qian, J., Gao, Y., Wei, W., Zhao, J., & Li, W. (2022, September). SepMLP: An all-MLP architecture for music source separation. In Proceedings of the 9th Conference on Sound and Music Technology: Revised Selected Papers from CMST (pp. 31-41). Singapore: Springer Nature Singapore.
Qian, J., Liu, X., Yu, Y., & Li, W. (2023). Stripe-Transformer: deep stripe feature learning for music source separation. EURASIP Journal on Audio, Speech, and Music Processing, 2023(1), 2.
Qiao, L., Tang, Q., & Sun, X. (2023, November). Music Recognition and Classification Recommendation Based on Convolutional Neural Network. In 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE) (pp. 1-7). IEEE.
Qin, Y., Xie, H., Ding, S., Tan, B., Li, Y., Zhao, B., & Ye, M. (2023). Bar transformer: a hierarchical model for learning long-term structure and generating impressive pop music. Applied Intelligence, 53(9), 10130-10148.
Quinto, R. J. M., Atienza, R. O., & Tiglao, N. M. C. (2017, November). Jazz music sub-genre classification using deep learning. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 3111-3116). IEEE.
Rahardwika, D. S., Rachmawanto, E. H., Sari, C. A., Irawan, C., Kusumaningrum, D. P., & Trusthi, S. L. (2020, September). Comparison of SVM, KNN, and NB classifier for genre music classification based on metadata. In 2020 international seminar on application for technology of information and communication (iSemantic) (pp. 12-16). IEEE.
Rajan, R., & Mohan, B. S. (2021). Distance Metric Learnt Kernel-Based Music Classification Using Timbral Descriptors. International Journal of Pattern Recognition and Artificial Intelligence, 35(13), 2151014.
Reddy, M. S., & Adilakshmi, T. (2014, January). Music recommendation system based on matrix factorization technique-SVD. In 2014 international conference on computer communication and informatics (pp. 1-6). IEEE.
Reddy, S. V., Krishna, V. R., Sapkal, R. J., Dhanke, J., Waghmare, S. P., & Kumar, K. (2024). A cutting-edge artificial intelligence paradigm for entertainment-infused music recommendations. Entertainment Computing, 51, 100717.
Ren, T., Wang, F., & Wang, H. (2020). A sequential naive Bayes method for music genre classification based on transitional information from pitch and beat. Statistics and Its Interface, 13(3), 361-371.
Richard, G., Chouteau, P., & Torres, B. (2024, April). A fully differentiable model for unsupervised singing voice separation. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 946-950). IEEE.
Roy, P., & Pachet, F. (2013, June). Enforcing meter in finite-length markov sequences. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 27, No. 1, pp. 854-861).
Sadek, R. A., Khalifa, A. A., & Elfattah, M. M. (2021, December). Detecting digital stimulant music using bidirectional deep long short term memory. In 2021 9th international Japan-Africa conference on electronics, communications, and computations (JAC-ECC) (pp. 150-154). IEEE.
Sajad, S., Dharshika, S., & Meleet, M. (2021, September). Music generation for novices using Recurrent Neural Network (RNN). In 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-6). IEEE.
Sánchez-Moreno, D., González, A. B. G., Vicente, M. D. M., Batista, V. F. L., & García, M. N. M. (2016). A collaborative filtering method for music recommendation using playing coefficients for artists and users. Expert Systems with Applications, 66, 234-244.
Sarmento, P., Kumar, A., Chen, Y. H., Carr, C. J., Zukowski, Z., & Barthet, M. (2023, April). GTR-CTRL: instrument and genre conditioning for guitar-focused music generation with transformers. In International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar) (pp. 260-275). Cham: Springer Nature Switzerland.
Scarpiniti, M., Sigismondi, E., Comminiello, D., & Uncini, A. (2023, September). A U-Net Based Architecture for Automatic Music Transcription. In 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
Schulze, W., & Van Der Merwe, B. (2011). Music generation with Markov models. IEEE MultiMedia, 18(03), 78-85.
Schulze-Forster, K., Richard, G., Kelley, L., Doire, C. S., & Badeau, R. (2023). Unsupervised music source separation using differentiable parametric source models. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1276-1289. Schwabe, M., & Heizmann, M. (2023). Improved Separation of Polyphonic Chamber Music Signals by Integrating Instrument Activity Labels. IEEE Access, 11, 42999-43007.
Schwabe, M., Murgul, S., & Heizmann, M. (2022). Dual task monophonic singing transcription. Journal of the Audio Engineering Society, 70(12), 1038-1047.
Seetharaman, P., Wichern, G., Venkataramani, S., & Le Roux, J. (2019, May). Class-conditional embeddings for music source separation. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 301-305). IEEE.
Senac, C., Pellegrini, T., Mouret, F., & Pinquier, J. (2017, June). Music feature maps with convolutional neural networks for music genre classification. In Proceedings of the 15th international workshop on content-based multimedia indexing (pp. 1-5).
Sgouros, T., Bousis, A., & Mitianoudis, N. (2022). An efficient short-time discrete cosine transform and attentive MultiResUNet framework for music source separation. IEEE Access, 10, 119448-119459.
Shen, C., Yao, V. Z., & Liu, Y. (2023, June). Everybody Compose: Deep Beats To Music. In Proceedings of the 14th Conference on ACM Multimedia Systems (pp. 353-357).
Shi, N., & Wang, Y. (2020). Symmetry in computer-aided music composition system with social network analysis and artificial neural network methods. Journal of Ambient Intelligence and Humanized Computing, 1-16.
Shih, Y. J., Wu, S. L., Zalkow, F., Müller, M., & Yang, Y. H. (2022). Theme transformer: Symbolic music generation with theme-conditioned transformer. IEEE Transactions on Multimedia, 25, 3495-3508.
Siddiquee, M. M. R., Rahman, M. S., Chowdhury, S. U. I., & Rahman, R. M. (2016). Association rule mining and audio signal processing for music discovery and recommendation. International Journal of Software Innovation (IJSI), 4(2), 71-87.
Siedenburg, K., Schädler, M. R., & Hülsmeier, D. (2019). Modeling the onset advantage in musical instrument recognition. The journal of the Acoustical Society of America, 146(6), EL523-EL529.
Sigtia, S., Benetos, E., & Dixon, S. (2016). An end-to-end neural network for polyphonic piano music transcription. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 24(5), 927-939.
Simonetta, F., Ntalampiras, S., & Avanzini, F. (2021, October). Audio-to-score alignment using deep automatic music transcription. In 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP) (pp. 1-6). IEEE.
Simonetta, F., Ntalampiras, S., & Avanzini, F. (2021, October). Audio-to-score alignment using deep automatic music transcription. In 2021 IEEE 23rd International Workshop on Multimedia Signal Processing (MMSP) (pp. 1-6). IEEE.
Singh, J., & Bohat, V. K. (2021, January). Neural network model for recommending music based on music genres. In 2021 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-6). IEEE.
Singh, J., & Ratnawat, A. (2018, December). Algorithmic Music Generation for the stimulation of Musical Memory in Alzheimer’s. In 2018 4th International Conference on Computing Communication and Automation (ICCCA) (pp. 1-4). IEEE.
Slizovskaia, O., Kim, L., Haro, G., & Gomez, E. (2019, May). End-to-end sound source separation conditioned on instrument labels. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 306-310). IEEE.
Soleymani, M., Aljanaki, A., Wiering, F., & Veltkamp, R. C. (2015, June). Content-based music recommendation using underlying music preference structure. In 2015 IEEE international conference on multimedia and expo (ICME) (pp. 1-6). IEEE.
Stasiak, B., & Papiernik, M. (2013). Melody-based approaches in music retrieval and recommendation systems. In Multimedia Services in Intelligent Environments: Recommendation Services (pp. 125-153). Heidelberg: Springer International Publishing.
Stefani, D., Peroni, S., & Turchet, L. (2022). A comparison of deep learning inference engines for embedded real-time audio classification. In Proceedings of the International Conference on Digital Audio Effects, DAFx (Vol. 3, pp. 256-263). MDPI (Multidisciplinary Digital Publishing Institute).
Steiner, P., Jalalvand, A., Stone, S., & Birkholz, P. (2021, January). Feature engineering and stacked echo state networks for musical onset detection. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 9537-9544). IEEE.
Stoller, D., Durand, S., & Ewert, S. (2019, May). End-to-end lyrics alignment for polyphonic music using an audio-to-character recognition model. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 181-185). IEEE.
Su, J. H., Chang, W. Y., & Tseng, V. S. (2017). Effective social content-based collaborative filtering for music recommendation. Intelligent Data Analysis, 21(S1), S195-S216.
Su, J. H., Chin, C. Y., Liao, Y. W., Yang, H. C., Tseng, V. S., & Hsieh, S. Y. (2020). A personalized music recommender system using user contents, music contents and preference ratings. Vietnam Journal of Computer Science, 7(01), 77-92.
Sudarma, M., & Harsemadi, I. G. (2017). Design and analysis system of KNN and ID3 algorithm for music classification based on mood feature extraction. International Journal of Electrical and Computer Engineering, 7(1), 486.
Sun, J. (2022). Personalized music recommendation algorithm based on spark platform. Computational Intelligence and Neuroscience, 2022(1), 7157075.
Sunitha, M., & Adilakshmi, T. (2018). Music recommendation system with user-based and item-based collaborative filtering technique. In Networking Communication and Data Knowledge Engineering: Volume 1 (pp. 267-278). Springer Singapore.
Suprapto, Y. K., & Triwidyastuti, Y. (2015). Saron music transcription based on rhythmic information using hmm on gamelan orchestra. TELKOMNIKA (Telecommunication Computing Electronics and Control), 13(1), 103-117.
Tahmasebi, S., Gajȩcki, T., & Nogueira, W. (2020). Design and evaluation of a real-time audio source separation algorithm to remix music for cochlear implant users. Frontiers in Neuroscience, 14, 434.
Tanberk, S., & Tükel, D. B. (2021, January). Style-specific Turkish pop music composition with CNN and LSTM network. In 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000181-000185). IEEE.
Tang, H., & Chen, N. (2020). Combining CNN and broad learning for music classification. IEICE TRANSACTIONS on Information and Systems, 103(3), 695-701.
Tang, J., Yin, L., & Yu, J. (2023, June). Partially Trained Music Generation based on Transformer. In 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) (pp. 1133-1138). IEEE.
Telila, Y., Cucinotta, T., & Bacciu, D. (2023). Automatic Music Transcription using Convolutional Neural Networks and Constant-Q Transform. In Ital-IA (pp. 526-531).
Thambi, S. V., Sreekumar, K. T., Kumar, C. S., & Raj, P. R. (2014, December). Random forest algorithm for improving the performance of speech/non-speech detection. In 2014 First International Conference on Computational Systems and Communications (ICCSC) (pp. 28-32). IEEE.
Tian, D., Chen, J., Gao, Z., & Pan, G. (2023, September). Linear Transformer-GAN: A Novel Architecture to Symbolic Music Generation. In International Conference on Artificial Neural Networks (pp. 451-463). Cham: Springer Nature Switzerland.
Tian, D., Chen, J., Gao, Z., & Pan, G. (2023, September). Linear Transformer-GAN: A Novel Architecture to Symbolic Music Generation. In International Conference on Artificial Neural Networks (pp. 451-463). Cham: Springer Nature Switzerland.
Tian, L., & Liu, F. (2023). Design of Music Heritage Database System Based on Collaborative Filtering Algorithm. In Advances in Artificial Intelligence, Big Data and Algorithms (pp. 438-443). IOS Press.
Tie, Y., Wang, T., Jin, C., Li, X., & Yang, P. (2022). Wireless Communications and Mobile Computing Multitrack Music Generation Network Based on Music Rules. Wireless Communications and Mobile Computing, 2022(1), 5845689.
Tjahyanto, A., Suprapto, Y. K., Purnomo, M. H., & Wulandari, D. P. (2012, May). Fft-based features selection for javanese music note and instrument identification using support vector machines. In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE) (Vol. 1, pp. 439-443). IEEE.
Tomaz Neves, P. L., Fornari, J., & Batista Florindo, J. (2022). Self-attention generative adversarial networks applied to conditional music generation. Multimedia Tools and Applications, 81(17), 24419-24430.
Vall, A., Skowron, M., Knees, P., & Schedl, M. (2015). Improving Music Recommendations with a Weighted Factorization of the Tagging Activity. In ISMIR (pp. 65-71).
Van den Oord, A., Dieleman, S., & Schrauwen, B. (2013). Deep content-based music recommendation. Advances in neural information processing systems, 26.
Vemula, D.R., Tripathi, S.K., Sharma, N.K., Hussain, M.M., Swamy, U.R., Polavarapu, B.L. (2023). Music Generation Using Deep Learning. In: Kumar Singh, K., Bajpai, M.K., Sheikh Akbari, A. (eds) Machine Vision and Augmented Intelligence. Lecture Notes in Electrical Engineering, vol 1007. Springer, Singapore
Venkatachalam, N. (2024, January). MusiciAI: A Hybrid Generative Model for Music Therapy using Cross-Modal Transformer and Variational Autoencoder. In 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) (pp. 1176-1180). IEEE.
Verma, J. P., Bhattacharya, P., Rathor, A. S., Shah, J., & Tanwar, S. (2022, July). Collaborative filtering-based music recommendation in view of negative feedback system. In Proceedings of Third International Conference on Computing, Communications, and Cyber-Security: IC4S 2021 (pp. 447-460). Singapore: Springer Nature Singapore.
Wahbi, I. S., Bahbouh, H. T., & Yahia, K. M. (2023, March). Transcription of Arabic and Turkish Music Using Convolutional Neural Networks. In 2023 5th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE) (Vol. 5, pp. 1-5). IEEE.
Wang, C., & Geng, X. (2023, June). Simulation of music style classification model based on support vector machine algorithm. In Sixth International Conference on Intelligent Computing, Communication, and Devices (ICCD 2023) (Vol. 12703, pp. 266-272). SPIE.
Wang, H., Han, S., Li, G., & Zhao, B. (2022, October). A Hybrid Neural Network for Music Generation Using Frequency Domain Data. In International Conference on Artificial Intelligence in China (pp. 219-228). Singapore: Springer Nature Singapore.
Wang, H., Hao, S., Zhang, C., Wang, X., & Chen, Y. (2023). Motif transformer: Generating music with motifs. IEEE Access, 11, 63197-63204.
Wang, J. (2023). Research on the Integration Path of College Vocal Music Teaching and Traditional Music Culture Based on Deep Learning. Applied Mathematics and Nonlinear Sciences.
Wang, L., Zhu, H., Zhang, X., Li, S., & Li, W. (2020). Transfer learning for music classification and regression tasks using artist tags. In Proceedings of the 7th Conference on Sound and Music Technology (CSMT) Revised Selected Papers (pp. 81-89). Springer Singapore.
Wang, S., Xu, C., Ding, A. S., & Tang, Z. (2021). A novel emotion-aware hybrid music recommendation method using deep neural network. Electronics, 10(15), 1769.
Wang, T., Liu, J., Jin, C., Li, J., & Ma, S. (2020, October). An intelligent music generation based on Variational Autoencoder. In 2020 International Conference on Culture-oriented Science & Technology (ICCST) (pp. 394-398). IEEE.
Wang, W., Li, J., Li, Y., & Xing, X. (2024). Style-conditioned music generation with Transformer-GANs. Frontiers of Information Technology & Electronic Engineering, 25(1), 106-120.
Wang, X. (2020). Research on recognition and classification of folk music based on feature extraction algorithm. Informatica, 44(4).
Wang, X., & Wang, Y. (2014, November). Improving content-based and hybrid music recommendation using deep learning. In Proceedings of the 22nd ACM international conference on Multimedia (pp. 627-636).
Wang, X., Jin, C., & Zhao, W. (2018). Beijing opera synthesis based on straight algorithm and deep learning. Advances in Multimedia, 2018(1), 5158164.
Wang, Y. (2023). Piano automatic transcription based on transformer. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-8.
Wang, Y. A., Huang, Y. K., Lin, T. C., Su, S. Y., & Chen, Y. N. (2019, May). Modeling melodic feature dependency with modularized variational auto-encoder. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 191-195). IEEE.
Wang, Z., Wang, D., Zhang, Y., & Xia, G. (2020). Learning interpretable representation for controllable polyphonic music generation. Proceedings of the 21st International Society for Music Information Retrieval Conference, ISMIR 2020, pp. 795–802
Wei, X., Chen, J., Zheng, Z., Guo, L., Li, L., & Wang, D. (2023). A Multi-Scale Attentive Transformer for Multi-Instrument Symbolic Music Generation. arXiv preprint arXiv:2305.16592.
Weiß, C., & Peeters, G. (2022). Comparing deep models and evaluation strategies for multi-pitch estimation in music recordings. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 30, 2814-2827.
Wiafe, A., Nutrokpor, C., Owusu, E., Kastriku, F. A., & Wiafe, I. (2022). Using genetic algorithms for music composition: implications of early termination on aesthetic quality. International Journal of Information Technology, 14(4), 1875-1881.
Williams, D., Hodge, V. J., Gega, L., Murphy, D., Cowling, P. I., & Drachen, A. (2019, March). AI and automatic music generation for mindfulness. In 2019 AES International Conference on Immersive and Interactive Audio: Creating the Next Dimension of Sound Experience. York.
Wu, C. (2024). Music Therapy Music Selection Based on Big Data Analysis. Applied Mathematics and Nonlinear Sciences, 9(1). Wu, G., Liu, S., & Fan, X. (2023). The power of fragmentation: a hierarchical transformer model for structural segmentation in symbolic music generation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1409-1420.
Wu, H., & Zhu, L. (2024). Adaptive classification method of electronic music based on improved decision tree. International Journal of Arts and Technology, 15(1), 1-12.
Wu, S. L., & Yang, Y. H. (2023). MuseMorphose: Full-song and fine-grained piano music style transfer with one transformer VAE. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1953-1967.
Wu, S. L., & Yang, Y. H. (2023, June). Compose & Embellish: Well-structured piano performance generation via a two-stage approach. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
Wu, Y. T., Chen, B., & Su, L. (2020). Multi-instrument automatic music transcription with self-attention-based instance segmentation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 2796-2809.
Xia, J. (2022). Construction and implementation of music recommendation model utilising deep learning artificial neural network and mobile edge computing. International journal of grid and utility computing, 13(2-3), 183-194.
Xiang, M. (2022, November). Music Vocal Spectrum Classification Model based on Improved SVM Algorithm. In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1244-1247). IEEE.
Xiao, Z., Chen, X., & Zhou, L. (2023). Polyphonic piano transcription based on graph convolutional network. Signal Processing, 212, 109134.
Xin, Y. (2024). MusicEmo: transformer-based intelligent approach towards music emotion generation and recognition. Journal of Ambient Intelligence and Humanized Computing, 1-11.
Xu, L., Chen, Y., & Zhang, L. (2023, August). Intelligent Composition Based on Fibonacci Sequence and Hidden Markov Model. In Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology (pp. 1-6).
Xu, W., McAuley, J., Dubnov, S., & Dong, H. W. (2023, December). Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4512-4517). IEEE.
Xu, Y. (2024). Enhancing Music Generation With a Semantic-Based Sequence-to-Music Transformer Framework. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1-19.
Xu, Y., Wang, W., Cui, H., Xu, M., & Li, M. (2022). Paralinguistic singing attribute recognition using supervised machine learning for describing the classical tenor solo singing voice in vocal pedagogy. EURASIP Journal on Audio, Speech, and Music Processing, 2022(1), 8.
Yadav, N., Kumar Singh, A., & Pal, S. (2022). Improved self‐attentive Musical Instrument Digital Interface content‐based music recommendation system. Computational Intelligence, 38(4), 1232-1257.
Yang, R., Chen, T., Zhang, Y., & Xia, G. (2019). Inspecting and interacting with meaningful music representations using VAE. Proceedings of the International Conference on New Interfaces for Musical Expression, pp. 307–312 Yang, X., Yu, Y., & Wu, X. (2022). Double linear transformer for background music generation from videos. Applied Sciences, 12(10), 5050.
Ycart, A., & Benetos, E. (2020). Learning and evaluation methodologies for polyphonic music sequence prediction with LSTMs. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 1328-1341.
Ycart, A., McLeod, A., Benetos, E., & Yoshii, K. (2019). Blending acoustic and language model predictions for automatic music transcription.
Yi, J., Zhu, Y., Xie, J., & Chen, Z. (2021). Cross-modal variational auto-encoder for content-based micro-video background music recommendation. IEEE Transactions on Multimedia, 25, 515-528.
Yi, Y., Zhu, X., Yue, Y., & Wang, W. (2021, April). Music genre classification with LSTM based on time and frequency domain features. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. 678-682). IEEE.
Yi, Y., Zhu, X., Yue, Y., & Wang, W. (2021, April). Music genre classification with LSTM based on time and frequency domain features. In 2021 IEEE 6th International Conference on Computer and Communication Systems (ICCCS) (pp. 678-682). IEEE.
Yiiksel, A. Ç., Karci, M. M., & Uyar, A. Ş. (2011, June). Automatic music generation using evolutionary algorithms and neural networks. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 354-358). IEEE.
Yin, C. (2024). Application of Recommendation Algorithms Based on Social Relationships and Behavioral Characteristics in Music Online Teaching. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 19(1), 1-18.
Yin, Z., Reuben, F., Stepney, S., & Collins, T. (2023). Deep learning’s shallow gains: A comparative evaluation of algorithms for automatic music generation. Machine Learning, 112(5), 1785-1822.
Yingjun Gong; (2021). Application of virtual reality teaching method and artificial intelligence technology in digital media art creation. Ecological Informatics.
Yu, B., Lu, P., Wang, R., Hu, W., Tan, X., Ye, W., ... & Liu, T. Y. (2022). Museformer: Transformer with fine-and coarse-grained attention for music generation. Advances in Neural Information Processing Systems, 35, 1376-1388.
Yu, D., Duan, H., Fang, J., & Zeng, B. (2020). Predominant instrument recognition based on deep neural network with auxiliary classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28, 852-861.
Yuan, Y., & Liu, J. (2024). Music Classification and Identification Based on Convolutional Neural Network, Computer-Aided Design and Applications , 21(S18), pp. 205–221
Zehren, M., Alunno, M., & Bientinesi, P. (2023). High-quality and reproducible automatic drum transcription from crowdsourced data. Signals, 4(4), 768-787.
Zhang, J. (2021). Music feature extraction and classification algorithm based on deep learning. Scientific Programming, 2021(1), 1651560.
Zhang, N. (2023). Learning adversarial transformer for symbolic music generation. IEEE transactions on neural networks and learning systems, 34(4), 1754-1763.
Zhang, R., Bai, J., Guan, X., Li, Q. (2021). Music Source Separation Method Based on Unet Combining SE and BiSRU. Huanan Ligong Daxue Xuebao/Journal of South China University of Technology (Natural Science), 49(11).
Zhang, T. (2024). Application of Unet-SE-Bisru Algorithm for Music Signal Processing in Music Source Separation. Scalable Computing: Practice and Experience, 25(4), 2960-2972.
Zhang, T., & Liu, S. (2022). [Retracted] Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph. Security and Communication Networks, 2022(1), 5889724.
Zhang, Y., Lv, X., Li, Q., Wu, X., Su, Y., & Yang, H. (2024). An automatic music generation method based on RSCLN_Transformer network. Multimedia Systems, 30(1), 4.
Zhang, Z., Yu, Y., & Takasu, A. (2023). Controllable lyrics-to-melody generation. Neural Computing and Applications, 35(27), 19805-19819.
Zhao, W., & Yin, B. (2022, January). Environmental sound classification based on pitch shifting. In 2022 International Seminar on Computer Science and Engineering Technology (SCSET) (pp. 275-280). IEEE.
Zhao, W., Zhou, Y., Tie, Y., & Zhao, Y. (2018, October). Recurrent neural network for MIDI music emotion classification. In 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (pp. 2596-2600). IEEE.
Zheng, H. T., Chen, J. Y., Liang, N., Sangaiah, A. K., Jiang, Y., & Zhao, C. Z. (2019). A deep temporal neural music recommendation model utilizing music and user metadata. Applied Sciences, 9(4), 703.
Zheng, L., & Li, C. (2024). Real-Time Emotion-Based Piano Music Generation using Generative Adversarial Network (GAN). IEEE Access.
Zheng, R., Liu, X., & Butala, M. D. (2023, January). Multi-Instrument Polyphonic Melody Transcription Based on Deep Learning. In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 762-767). IEEE.
Zhou, J., Niu, T., Zhu, H., & Wang, X. (2023). CoCoFormer: A controllable feature-rich polyphonic music generation method. In Summit on Music Intelligence (pp. 95-107). Singapore: Springer Nature Singapore.
Zhu, Q., Shankar, A., & Maple, C. (2024). Grey wolf optimizer based deep learning mechanism for music composition with data analysis. Applied Soft Computing, 153, 111294.
Zhu, Y., Olszewski, K., Wu, Y., Achlioptas, P., Chai, M., Yan, Y., & Tulyakov, S. (2022, October). Quantized gan for complex music generation from dance videos. In European Conference on Computer Vision (pp. 182-199). Cham: Springer Nature Switzerland.
Ziegler, Z., & Rush, A. (2019, May). Latent normalizing flows for discrete sequences. In International Conference on Machine Learning (pp. 7673-7682). PMLR.
Zou, Y., Zou, P., Zhao, Y., Zhang, K., Zhang, R., & Wang, X. (2022, May). Melons: generating melody with long-term structure using transformers and structure graph. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 191-195). IEEE.
Zulfikar, W. B., Gerhana, Y. A., Almi, A. Y. P., Maylawati, D. S. A., & Al Amin, M. I. (2023, November). Mood of Song Detection Using Mel Frequency Cepstral Coefficient and Convolutional Neural Network with Tuning Hyperparameter. In 2023 11th International Conference on Cyber and IT Service Management (CITSM) (pp. 1-6). IEEE.
