On Optimal Multichannel Mean-Squared Error Estimators for Speech Enhancement
| Autor: | Jesper Jensen, Richard Heusdens, Richard C. Hendriks, Ulrik Kjems |
|---|---|
| Rok vydání: | 2009 |
| Předmět: |
noise reduction
Mean squared error Applied Mathematics Speech recognition Noise reduction Wiener filter Estimator Data_CODINGANDINFORMATIONTHEORY MMSE Discrete Fourier transform Speech enhancement Noise symbols.namesake multichannel Gaussian noise Signal Processing symbols Electrical and Electronic Engineering Algorithm Computer Science::Information Theory Mathematics |
| Zdroj: | IEEE Signal Procesing Letters, 16 (10), 2009 |
| ISSN: | 1558-2361 1070-9908 |
| DOI: | 10.1109/lsp.2009.2026205 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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