A General Flexible Framework for the Handling of Prior Information in 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 audio source separation framework based on a library of structured source models that enable the incorporation of prior knowledge about each source via user-specifiable constraints. While this framework generalizes several existing audio source separation methods, it also allows to imagine and implement new efficient methods that were not yet reported in the literature. We first introduce the framework by describing the model structure and constraints, explaining its generality, and summarizing its algorithmic 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 problems. We have released a software tool named Flexible Audio Source Separation Toolbox (FASST) implementing a baseline version of the framework in Matlab.
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Alexey Ozerov, Emmanuel Vincent, Frédéric Bimbot. A General Flexible Framework for the Handling of Prior Information in Audio Source Separation. IEEE Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2012, 20 (4), pp.1118 - 1133. ⟨10.1109/TASL.2011.2172425⟩. ⟨hal-00626962v4⟩

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