Denoising and Enhancement Speech Signal Using Wavelet
Subject Areas : Signal Processing
1 - University of Batna 2 (UB2)
Keywords: Wavelet Transform, Speech Enhancement, Denoising, Discrete Wavelet Ttransforms (DWT), Noise Reduction in Speech Signals.,
Abstract :
Speech enhancement aims to improve the quality and intelligibility of speech using various techniques and algorithms. The speech signal is always accompanied by background noise. The speech and communication processing systems must apply effective noise reduction techniques in order to extract the desired speech signal from its corrupted speech signal. In this project we study wavelet and wavelet transform, and the possibility of its employment in the processing and analysis of the speech signal in order to enhance the signal and remove noise of it. We will present different algorithms that depend on the wavelet transform and the mechanism to apply them in order to get rid of noise in the speech, and compare the results of the application of these algorithms with some traditional algorithms that are used to enhance the speech. The basic principles of the wavelike transform are presented as an alternative to the Fourier transform. Or immediate switching of the window The practical results obtained are based on processing a large database dedicated to speech bookmarks polluted with various noises in many SNRs. This article tends to be an extension of practical research to improve speech signal for hearing aid purposes. Also learn about the main frequency of letters and their uses in intelligent systems, such as voice control systems.
[1] Rupali V. Mane, and Dr.M.T.Kolte, " Implementation of Adaptive Filtering Algorithm for Speech Signal on FPGA " , International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 2, Issue3, March 2014.#
[2] J. Kim, " Time-frequency characterization of hand-transmitted, impulsive vibrations using analytic wavelet transform", Journal of Sound and Vibration, vol.308, pp. 98-111, Nov 2007.#
[3] M. HemaLatha and Dr.S, " Resolution enhancement of low resolution satellite images using Dual tree complex wavelet transform", International Journal of Scientific & Engineering Research, Volume 8, Issue 5, May-2017.#
[4] I. Daubechies, Ten Lectures on Wavelets. Philadelphia: SIAM, 1992.#
[5] U. Jayakrishnan, G. Dhavale and P. Khandelwal, Wavelet Denoising Of Discrete-Time Signals, EE678 Wavelets Application Assignment, 2005.#
[6] Y. Hoshino, Wavelet Transform Analysis the Recognizing Brain Activities for Development the Palm-Size and Simplification Near-Infrared Spectroscopy Prototype System by Using Arduino,2018.#
[7] K. Borna, and S. Palizdar, "Short Time Price Forecasting for Electricity Market Based on Hybrid Fuzzy Wavelet Transform and Bacteria Foraging Algorithm" Journal of Information Systems and Telecommunication, Vol. 4, No. 4, October-December 2016.#
[8] S. Rani, and R. Kaur, "review: audio noise reduction using filters and discrete wavelet transformation", Journal of The International Association of Advanced Technology and Science (JIAATS), ISSN-5563 1682, Vol. 16 , June 2015.#
[9] C. Gargour, M. Gabrea, V. Ramachandran, and J. Lina, “A Short Introduction to Wavelets and Their Applications”, IEEE Circuits and Systems Magazine, ISSN: 1531-636X, vol. 2, pp. 57-67, 2009.#
[10] R. Torkamani, and Sadeghzadeh, "Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing", Journal of Information Systems and Telecommunication, Vol. 7, No. 2, April-June 2019.#
[11] N. Kaladharan, "Speech Enhancement by Spectral Subtraction Method", International Journal of Computer Applications (0975 – 8887) Volume 96– No.13, June 2014.#
[12] P. Goli, "Speech Intelligibility Improvement in Noisy Environments for Near-End Listening Enhancement", Journal of Information Systems and Telecommunication, Vol. 4, No. 1, January-March 2016.#
[13] M. R. Kahrizi, "Long-Term Spectral Pseudo-Entropy (LTSPE): A New Robust Feature For Speech Activity Detection", Journal of Information Systems and Telecommunication, Vol. 6, No. 4, October-December 2018.#
[14] K. Wang, "Wavelet packet analysis for speaker-independent emotion recognition", Neurocomputing 398 (2020) 257–264.#
[15] A. Bhattacharyya, " Fourier–Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals ", m5G; v1.232; Prn:7/03/2018; 12:30] P.1 (1-12).#
[16] A. Upadhyay, and R.B. Pachori, "Speech enhancement based on mEMD–VMD method, Electron", ELECTRONICS LETTERS 30th March 2017 Vol. 53 No. 7 pp. 502–504.#
[17] P. Singh, S.D. Joshi, R.K. Patney, and K. Saha, "The Fourier decomposition method for nonlinear and non stationary time series analysis", Proc. R. Soc. A 473 (2017).#
[18] Yaseen, G. Young Son, and Soonil Kwon," Classification of Heart Sound Signal UsingMultiple Features", Appl. Sci. 2018, 8, 2344; doi:10.3390/app8122344.#
[19]M, Pourseiedrezaei, "Prediction of Psychoacoustic Metrics Using Combination of Wavelet Packet Transform and an Optimized Artificial Neural Network ", Archives of Acoustics – Volume 44, Number 3, 2019.#
