A Laplacian-based MMSE estimator for speech enhancement

This paper focuses on optimal estimators of the magnitude spectrum for speech enhancement. We present an analytical solution for estimating in the MMSE sense the magnitude spectrum when the clean speech DFT coefficients are modeled by a Laplacian distribution and the noise DFT coefficients are modeled by a Gaussian distribution. Furthermore, we derive the MMSE estimator under speech presence uncertainty and a Laplacian statistical model. Results indicated that the Laplacian-based MMSE estimator yielded less residual noise in the enhanced speech than the traditional Gaussian-based MMSE estimator. Overall, the present study demonstrates that the assumed distribution of the DFT coefficients can have a significant effect on the quality of the enhanced speech.

[1]  John H. L. Hansen,et al.  An effective quality evaluation protocol for speech enhancement algorithms , 1998, ICSLP.

[2]  Hyochoong Bang,et al.  The Finite Element Method Using MATLAB , 1996 .

[3]  I. M. Pyshik,et al.  Table of integrals, series, and products , 1965 .

[4]  T. Lotter.  NOISE REDUCTION BY MAXIMUM A POSTERIORI SPECTRAL AMPLITUDE ESTIMATION WITH SUPERGAUSSIAN SPEECH MODELING , 2003 .

[5]  Bruno O. Shubert,et al.  Random variables and stochastic processes , 1979 .

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

[7]  Yi Hu,et al.  Evaluation of objective measures for speech enhancement , 2006, INTERSPEECH.

[8]  I. Miller.  Probability, Random Variables, and Stochastic Processes , 1966 .

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

[10]  Rainer Martin,et al.  Speech enhancement using MMSE short time spectral estimation with gamma distributed speech priors , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Rainer Martin,et al.  MMSE estimation of magnitude-squared DFT coefficients with superGaussian priors , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

[12]  Rainer Martin,et al.  SPEECH ENHANCEMENT IN THE DFT DOMAIN USING LAPLACIAN SPEECH PRIORS , 2003 .

[13]  Steven F. Boll,et al.  Optimal estimators for spectral restoration of noisy speech , 1984, ICASSP.

[14]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[15]  A. B.,et al.  SPEECH COMMUNICATION , 2001 .

[16]  Hyochoong Bang,et al.  The finite element method using MATLAB (2nd ed.) , 2000 .

[17]  Ye Li,et al.  Speech Enhancement for Non-Stationary Noise Environments , 2009, 2009 International Conference on Information Engineering and Computer Science.

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

[19]  Rainer Martin,et al.  Speech enhancement based on minimum mean-square error estimation and supergaussian priors , 2005, IEEE Transactions on Speech and Audio Processing.

[20]  Bin Chen,et al.  Speech enhancement using a MMSE short time spectral amplitude estimator with Laplacian speech modeling , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..