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Reliable A posteriori Signal-to-Noise Ratio features selection

Cyril Plapous 1 Claude Marro 1 Pascal Scalart 2 
2 R2D2 - Reconfigurable and Retargetable Digital Devices
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, INRIA Rennes, ENSSAT - École Nationale Supérieure des Sciences Appliquées et de Technologie
Abstract : This paper adresses the problem of single microphone speech enhancement in noisy environments. State of the art short-time noise reduction techniques are most often expressed as a spectral gain depending on the Signal-to-Noise Ratio (SNR). The well-known decision-directed (DD) approcah drastically limits the level of musical noise but the estimated a priori SNR is biased since it depends on the speech spectrum estimated in the previous frame. The consequence of this biais is an annoying reverberation effect. We propose a new method, called Reliable Features Selection Noise Reduction (RFSNR) technique, that is able to classify the a posteriori SNR estimates into two categories: the reliable features leading to speech components and the unrealiable ones corresponding to musical noise only. Then it is possible to directly enhance speech using a posteriori SNR leading to an unbiased estimator.
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Submitted on : Tuesday, May 11, 2010 - 10:25:56 AM
Last modification on : Friday, February 4, 2022 - 3:22:13 AM
Long-term archiving on: : Thursday, September 16, 2010 - 1:56:57 PM

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  • HAL Id : inria-00482648, version 1

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Cyril Plapous, Claude Marro, Pascal Scalart. Reliable A posteriori Signal-to-Noise Ratio features selection. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Oct 2005, Mohonc Mountain House, New Paltz, New York, United States. ⟨inria-00482648⟩

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