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On Optimal Multichannel Mean-Squared Error Estimators for Speech Enhancement

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4 Author(s)
Hendriks, R.C. ; Dept. of Mediamatics, Delft Univ. of Technol., Delft, Netherlands ; Heusdens, R. ; Kjems, U. ; Jensen, J.

In this letter we present discrete Fourier transform (DFT) domain minimum mean-squared error (MMSE) estimators for multichannel noise reduction. The estimators are derived assuming that the clean speech magnitude DFT coefficients are generalized-Gamma distributed. We show that for Gaussian distributed noise DFT coefficients, the optimal filtering approach consists of a concatenation of a minimum variance distortionless response (MVDR) beamformer followed by well-known single-channel MMSE estimators. The multichannel Wiener filter follows as a special case of the presented MSE estimators and is in general suboptimal. For non-Gaussian distributed noise DFT coefficients the resulting spatial filter is in general nonlinear with respect to the noisy microphone signals and cannot be decomposed into an MVDR beamformer and a post-filter.

Published in:

Signal Processing Letters, IEEE  (Volume:16 ,  Issue: 10 )