Musical Noise Reduction Capability of Various Speech Enhancement Algorithms

This paper presents a comparative analysis of spectral subtraction and Weiner denoising techniques for musical noise reduction. The iterative spectral subtraction method provides least musical noise generation applied in different noisy environments. The method of musical noise production is traced by observing the change in the kurtosis ratio of noise spectrum using different denoising techniques for different noisy signal. A MATLAB simulation is performed for four different noisy environments car noise, babble noise, operation room noise and machine gun noise at −10, −5, 0, 5 and 10 dB input SNR levels. It is observed that wiener based methods provide more improvement in SNR as compared to spectral subtraction based methods. But at the same time musical noise generation is more in wiener based methods. The wiener based method HRNR gives a maximum 35.77 dB improvement in SNR for car noise at −10 dB input SNR level. Iterative spectral subtraction gives the minimum value of kurtosis ratio for all noises at all input SNR level.

[1]  Richard M. Schwartz,et al.  Enhancement of speech corrupted by acoustic noise , 1979, ICASSP.

[2]  Philipos C. Loizou,et al.  Speech Enhancement: Theory and Practice , 2007 .

[3]  Pascal Scalart,et al.  Author manuscript, published in "IEEE Transactions on Audio, Speech, and Language Processing (2006)" 1 Improved Signal-to-Noise Ratio Estimation for Speech Enhancement , 2010 .

[4]  Tetsuya Shimamura,et al.  Improved spectral subtraction utilizing iterative processing , 2007 .

[5]  Olivier Cappé,et al.  Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor , 1994, IEEE Trans. Speech Audio Process..

[6]  Pascal Scalart,et al.  A two-step noise reduction technique , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Yang Lu,et al.  A geometric approach to spectral subtraction , 2008, Speech Commun..

[8]  Prateek Saxena,et al.  Comparative analysis of speech enhancement methods , 2013, 2013 Tenth International Conference on Wireless and Optical Communications Networks (WOCN).

[9]  Kiyohiro Shikano,et al.  Musical noise generation analysis for noise reduction methods based on spectral subtraction and MMSE STSA estimation , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  Kiyohiro Shikano,et al.  Automatic optimization scheme of spectral subtraction based on musical noise assessment via higher-order statistics , 2008 .

[11]  Kohei Yamashita,et al.  Spectral subtraction iterated with weighting factors , 2002, Speech Coding, 2002, IEEE Workshop Proceedings..

[12]  Rainer Martin,et al.  Spectral Subtraction Based on Minimum Statistics , 2001 .

[13]  T. Hasan,et al.  Iterative noise power subtraction technique for improved speech quality , 2008, 2008 International Conference on Electrical and Computer Engineering.

[14]  Pascal Scalart,et al.  Speech enhancement using harmonic regeneration , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[15]  David Malah,et al.  Speech enhancement using a minimum mean-square error log-spectral amplitude estimator , 1984, IEEE Trans. Acoust. Speech Signal Process..

[16]  R. McAulay,et al.  Speech enhancement using a soft-decision noise suppression filter , 1980 .

[17]  Kiyohiro Shikano,et al.  Musical-Noise-Free Speech Enhancement Based on Optimized Iterative Spectral Subtraction , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[18]  Guo Li,et al.  Improved Voice Activity Detection Based on Iterative Spectral Subtraction and Double Thresholds for CVR , 2008, 2008 Workshop on Power Electronics and Intelligent Transportation System.

[19]  S. Boll,et al.  Suppression of acoustic noise in speech using spectral subtraction , 1979 .