Reliable A posteriori Signal-to-Noise Ratio features selection

Cyril Plapous 1 Claude Marro 1 Pascal Scalart 2
1 Orange Labs [Lannion]
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.
Type de document :
Communication dans un congrès
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Oct 2005, Mohonc Mountain House, New Paltz, New York, United States. 2005
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Contributeur : Pascal Scalart <>
Soumis le : mardi 11 mai 2010 - 10:25:56
Dernière modification le : vendredi 16 novembre 2018 - 01:25:00
Document(s) archivé(s) le : jeudi 16 septembre 2010 - 13:56:57

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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. 2005. 〈inria-00482648〉

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