An improved speech enhancement approach based on combination of compressed sensing and Kalman filter

This paper reviews some existing Speech Enhancement techniques and also proposes a new method for enhancing the speech by combining Compressed Sensing and Kalman filter approaches. This approach is based on reconstruction of noisy speech signal using Compressive Sampling Matching Pursuit (CoSaMP) algorithm and further enhanced by Kalman filter. The performance of the proposed method is evaluated and compared with that of the existing techniques in terms of intelligibility and quality measure parameters of speech. The proposed algorithm shows an improved performance compared to Spectral Subtraction, MMSE, Wiener filter, Signal Subspace, Kalman filter in terms of WSS, LLR, SegSNR, SNRloss, PESQ and overall quality.

[1]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[2]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.

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

[4]  Xiuhua Geng,et al.  A signal subspace approach for speech enhancement , 2014 .

[5]  Yi Hu,et al.  Evaluation of Objective Quality Measures for Speech Enhancement , 2008, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Yariv Ephraim,et al.  A signal subspace approach for speech enhancement , 1995, IEEE Trans. Speech Audio Process..

[7]  Pascal Scalart,et al.  Improved Signal-to-Noise Ratio Estimation for Speech Enhancement , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[8]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[9]  Wang Guang Yan,et al.  A Signal Subspace Speech Enhancement Method for Various Noises , 2013 .

[10]  Jonathon Shlens,et al.  A Tutorial on Principal Component Analysis , 2014, ArXiv.

[11]  Seyed Ghorshi,et al.  Compressed sensing based speech enhancement , 2014, 2014 8th International Conference on Signal Processing and Communication Systems (ICSPCS).

[12]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[13]  Ephraim Speech enhancement using a minimum mean square error short-time spectral amplitude estimator , 1984 .

[14]  Kuldip K. Paliwal,et al.  A speech enhancement method based on Kalman filtering , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  P. Sharma,et al.  Evaluating performance of Compressed Sensing for speech signals , 2013, 2013 3rd IEEE International Advance Computing Conference (IACC).

[16]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[17]  Richard G. Baraniuk,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[18]  Pranab Das,et al.  Performance Evaluation of Wiener Filter and Kalman Filter Combined with Spectral Subtraction in Speaker Verification System , 2013 .

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

[20]  Philipos C. Loizou,et al.  SNR loss: A new objective measure for predicting the intelligibility of noise-suppressed speech , 2011, Speech Commun..