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A general modular framework for audio source separation

Alexey Ozerov 1 Emmanuel Vincent 1 Frédéric Bimbot 1 
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
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 Expectation-Maximization algorithm. Finally, we illustrate the above-mentioned capabilities of the framework by applying it in several new and existing configurations to different source separation scenarios.
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Submitted on : Friday, January 7, 2011 - 2:42:38 PM
Last modification on : Friday, February 4, 2022 - 3:18:51 AM
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  • HAL Id : inria-00553504, version 1


Alexey Ozerov, Emmanuel Vincent, Frédéric Bimbot. A general modular framework for audio source separation. 9th International Conference on Latent Variable Analysis and Signal Separation (LVA/ICA'10), Sep 2010, Saint-Malo, France. ⟨inria-00553504⟩



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