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NONNEGATIVE MATRIX FACTORIZATION AND SPATIAL COVARIANCE MODEL FOR UNDERDETERMINED REVERBERANT AUDIO SOURCE SEPARATION
"... We address the problem of blind audio source separation in the underdetermined and convolutive case. The contribution of each source to the mixture channels in the timefrequency domain is modeled by a zeromean Gaussian random vector with a full rank covariance matrix composed of two terms: a vari ..."
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Cited by 15 (6 self)
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We address the problem of blind audio source separation in the underdetermined and convolutive case. The contribution of each source to the mixture channels in the timefrequency domain is modeled by a zeromean Gaussian random vector with a full rank covariance matrix composed of two terms: a variance which represents the spectral properties of the source and which is modeled by a nonnegative matrix factorization (NMF) model and another full rank covariance matrix which encodes the spatial properties of the source contribution in the mixture. We address the estimation of these parameters by maximizing the likelihood of the mixture using an expectationmaximization (EM) algorithm. Theoretical propositions are corroborated by experimental studies on stereo reverberant music mixtures. 1.
A general modular framework for audio source separation
 in "Proc. 9th Int. Conf. on Latent Variable Analysis and Signal Separation (LVA/ICA
"... Abstract. Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general modular audio source separation framework based on a libr ..."
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Cited by 8 (4 self)
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Abstract. Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general modular audio source separation framework based on a library of flexible source models that enable the incorporation of prior knowledge about the characteristics of each source. First, this framework generalizes several existing audio source separation methods, while bringing a common formulation for them. Second, it allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the flexible model, explaining its generality, and summarizing our modular implementation using a Generalized ExpectationMaximization algorithm. Finally, we illustrate the abovementioned capabilities of the framework by applying it in several new and existing configurations to different source separation scenarios.
Author manuscript, published in "9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA'10) (2010)" A General Modular Framework for Audio Source Separation
, 2011
"... Abstract. Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general modular audio source separation framework based on a libra ..."
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Abstract. Most of audio source separation methods are developed for a particular scenario characterized by the number of sources and channels and the characteristics of the sources and the mixing process. In this paper we introduce a general modular audio source separation framework based on a library of flexible source models that enable the incorporation of prior knowledge about the characteristics of each source. First, this framework generalizes several existing audio source separation methods, while bringing a common formulation for them. Second, it allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the flexible model, explaining its generality, and summarizing our modular implementation using a Generalized ExpectationMaximization algorithm. Finally, we illustrate the abovementioned capabilities of the framework by applying it in several new and existing configurations to different source separation scenarios. 1
ProjectTeam METISS Modélisation et Expérimentation pour le Traitement des Informations et des Signaux
"... c t i v it y e p o r t 2008 Table of contents ..."
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Combining Spectral Source Models for Audio Source Separation
, 2011
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