List of Articles Audio Signal Processing

• Open Access Article
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  1 - Long-Term Spectral Pseudo-Entropy (LTSPE): A New Robust Feature for Speech Activity Detection
  Mohammad Rasoul  kahrizi Seyed jahanshah kabudian
  10.7508/jist.2018.04.003
  Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of an audio signal including human speech. Applications of these types of systems include speech enhancement, noise cancellation, identification, r More

  Speech detection systems are known as a type of audio classifier systems which are used to recognize, detect or mark parts of an audio signal including human speech. Applications of these types of systems include speech enhancement, noise cancellation, identification, reducing the size of audio signals in communication and storage, and many other applications. Here, a novel robust feature named Long-Term Spectral Pseudo-Entropy (LTSPE) is proposed to detect speech and its purpose is to improve performance in combination with other features, increase accuracy and to have acceptable performance. To this end, the proposed method is compared to other new and well-known methods of this context in two different conditions, with uses a well-known speech enhancement algorithm to improve the quality of audio signals and without using speech enhancement algorithm. In this research, the MUSAN dataset has been used, which includes a large number of audio signals in the form of music, speech and noise. Also various known methods of machine learning have been used. As well as Criteria for measuring accuracy and error in this paper are the criteria for F-Score and Equal-Error Rate (EER) respectively. Experimental results on MUSAN dataset show that if our proposed feature LTSPE is combined with other features, the performance of the detector is improved. Moreover, this feature has higher accuracy and lower error compared to similar ones. Manuscript profile
• Open Access Article
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  2 - Onset Detection for Tar Solo Based on Pitch and Energy Features
  B. Farrokhi E. Kabir
  This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features and an adaptive peak picking algorithm is utilized for primary onset detection. An improved More

  This paper develops a new method of onset detection for the Tar, a traditional Iranian musical instrument. The proposed method is based on both types of pitch and energy features and an adaptive peak picking algorithm is utilized for primary onset detection. An improved template matching method is used to detect fundamental frequencies and finally, onsets are tagged based on primary onsets and fundamental frequencies. This step is especially useful to detect the reaz, repeatedly played notes with the same frequency and short durations. For the evaluation of the method, a data set with predetermined onsets was produced and the results were compared with an energy based method explained in terms of F measure. Manuscript profile