Under-Determined Reverberant Audio Source Separation Using Local Observed Covariance and Auditory-Motivated Time-Frequency Representation

Ngoc Duong 1 Emmanuel Vincent 1 Rémi Gribonval 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : We consider the local Gaussian modeling framework for under-determined convolutive audio source separation, where the spatial image of each source is modeled as a zero-mean Gaussian variable with full-rank time- and frequency- dependent covariance. We investigate two methods to improve the accuracy of parameter estimation, based on the use of local observed covariance and auditory-motivated time-frequency representation. We derive an iterative expectation-maximization (EM) algorithm with a suitable initialization scheme. Experimental results over stereo synthetic reverberant mixtures of speech show the effectiveness of the proposed methods.
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Communication dans un congrès
Vincent Vigneron and Vicente Zarzoso and Eric Moreau and Rémi Gribonval and Emmanuel Vincent. Latent Variable Analysis and Signal Separation, 9th International Conference on, Sep 2010, Saint-Malo, France. Springer, 6365, pp.73--80, 2010, Lecture Notes on Computer Science. 〈10.1007/978-3-642-15995-4_10〉
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Ngoc Duong, Emmanuel Vincent, Rémi Gribonval. Under-Determined Reverberant Audio Source Separation Using Local Observed Covariance and Auditory-Motivated Time-Frequency Representation. Vincent Vigneron and Vicente Zarzoso and Eric Moreau and Rémi Gribonval and Emmanuel Vincent. Latent Variable Analysis and Signal Separation, 9th International Conference on, Sep 2010, Saint-Malo, France. Springer, 6365, pp.73--80, 2010, Lecture Notes on Computer Science. 〈10.1007/978-3-642-15995-4_10〉. 〈inria-00541868〉

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