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Reseach Article

Noise Cancellation Method for Robust Speech Recognition

by Shajeesh. K. U., K. P. Soman
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 45 - Number 11
Year of Publication: 2012
Authors: Shajeesh. K. U., K. P. Soman
10.5120/6827-9438

Shajeesh. K. U., K. P. Soman . Noise Cancellation Method for Robust Speech Recognition. International Journal of Computer Applications. 45, 11 ( May 2012), 38-44. DOI=10.5120/6827-9438

@article{ 10.5120/6827-9438,
author = { Shajeesh. K. U., K. P. Soman },
title = { Noise Cancellation Method for Robust Speech Recognition },
journal = { International Journal of Computer Applications },
issue_date = { May 2012 },
volume = { 45 },
number = { 11 },
month = { May },
year = { 2012 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume45/number11/6827-9438/ },
doi = { 10.5120/6827-9438 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:37:35.979508+05:30
%A Shajeesh. K. U.
%A K. P. Soman
%T Noise Cancellation Method for Robust Speech Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 45
%N 11
%P 38-44
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Noise cancellation is the process of removing background noise from speech signal. The degradation of speech due to presence of background noise and several other noises cause difficulties in various signal processing tasks like speech recognition, speaker recognition, speaker verification etc. Many methods have been widely used to eliminate noise from speech signal like linear and nonlinear filtering methods, adaptive noise cancellation, total variation denoising etc. This paper addresses the problem of reducing the impulsive noise in speech signal using compressive sensing approach. The results are compared against three well known speech enhancement methods, spectral subtraction, Total variation denoising and signal dependent rank order mean algorithm. An automatic speech recognition system for Digits in Malayalam Language is implemented using MFCC and GMM. The impulse noise corrupted speech signal and the enhanced speech signal (the output of the noise cancellation system) are given as input to the classification system. The speech recognition system gives 12. 3 % accuracy for noisy signal where as 92. 3 % accuracy for the enhanced signal Objective and subjective quality evaluation are performed for the four speech enhancement scheme. Results show that the signal processed by the compressive sensing based method outperforms the other three methods.

References
  1. S. China Venkateswarlu, K. Satya Prasad and SubbaRami Reddy, "Improve Speech Enhancement Using Weiner Filtering", Global Journal of Computer Science and Technology, Vol. 11, Iss. 7, Ver 1. 0, May 2011.
  2. S. V. Vasighi and P. J. W. Rayner, "Detection and suppression of impulsive noise in speech communication systems,IEE Proc. of Communications, Speech and Vision, vol. 137, Pt. 1, no. 12, pp. 38-46, February 1990.
  3. Charu Chandra, Michael S. Moore and Sanjit K. Mitra, "An efficient method for the removal of impulse noise from speech and audio signals, IEEE Proc. on Circuits and Systems, vol. 4, no. 8, pp. 206-208, June 1998.
  4. Zhiyong He, Xuhong Guo, Maoqing Zhang, "Detection and Removal of Impulsive Colored Noise for Speech Enhancement," IEEE Proc. on Information and Automation, pp. 2320-2324, June 2010.
  5. Mital A. Gandhi, Christelle Ledoux, and Lamine Mili, "Robust Estimation Methods for Impulsive Noise Suppression in Speech," IEEE Proc. on Signal Processing and Information Technology, pp. 755-760, December 2005.
  6. Sina Zahedpour,Soheil Feizi,Arash Amini, Mahmoud Ferdosizadeh, and Faroh Marvasti, "Impulsive Noise Cancellation Based on Soft Decision and Recursion," IEEE Trans. on Instrumentation and Measurement, vol. 58, no. 8, pp. 2780-2789, August 2009.
  7. R. C. Nongpiur, "Impulse Noise Removal in Speech Using Wavelets," IEEE ICASSP, pp. 1593-1596, 2008.
  8. Massimo Fornasier and Holger Rauhut, "Compressive Sensing," April 18, 2010.
  9. Emmanuel J. Cands, and Michael B. Wakin, "An Introduction To Compressive Sampling," IEEE signal processing magazine, March 2008
  10. K. P. Soman and R. Ramanathan, 2012, 'Digital Signal and Image Processing- The sparse way', ELSEVIER Science and Technology book.
  11. Xiuzhi Guan, Yulong Gao, Jian Chang and Zhongzhao Zhang, "Advances in Theory of Compressive Sensing and Applications in Communication", 2011, First International Conference on Instrumentation, Measurement, Computer, Communication and Control, pp. 662-665, 21-23, October 2011.
  12. Emmanuel Cand`es and Justin Romberg "L1 magic:Recovery of Sparse Signals via Convex Programming", Caltech, October 2005.
  13. Monica FIRA, Liviu GORA, Constantin BARABASA, Nicolae CLEJU, "On ECG Compressed Sensing using Specific Overcomplete Dictionaries," Advances in Electrical and Computer Engineering Volume 10, Number 4, 2010.
  14. Hu, Y. , Loizou, P. C. , "Evaluation of Objective Quality Measures for Speech Enhancement". IEEE Trans. on audio, speech and language processing, Vol. 16, No. 1, pp. 229-238, January 2008.
  15. Kamil K. W´ojcicki, Benjamin J. Shannon and Kuldip K. Paliwal, "Spectral Subtraction with Variance Reduced Noise Spectrum Estimates," Signal Processing Laboratory Griffith University, Nathan Q4111, Australia,March 1984.
  16. Upadhyay, Navneet, Karmakar and Abhijit, "The spectral subtractive-type algorithms for enhancing speech in noisy environments," 2012, 1st International Conference on Recent Advances in Information Technology (RAIT) , pp. 841-847, 15-17, March 2012.
  17. Miyazaki. R, Saruwatari. H, Inoue. T, Takahashi Y, Shikano K and Kondo. K, "Musical-Noise-Free Speech Enhancement Based on Optimized Iterative Spectral Subtraction," IEEE Transactions on Audio, Speech, and Language Processing, 2012.
  18. Michael S. Moore and Sanjit K. Mitra, "Statistical Threshold Design for the Two-State Signal Dependent Rank Order Mean Filter", Department of Electrical and Computer Engineering, University of California , Santa Barbara.
  19. Ivan W. Selesnick and Ilker Bayram, "Total Variation Filtering," February 4, 2010.
  20. G. R Vogel and M. E. Oman, "Iterative methods for total variation denoising", SIAM J. Sci. Comput.
Index Terms

Computer Science
Information Sciences

Keywords

Speech Enhancement Compressive Sensing Over Complete Dictionary Quality Evaluation Metrics And Automatic Speech Recognition