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Underdetermined Source Separation with Structured Source Priors

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

We consider the source extraction problem for stereo instantaneous musical mixtures with more than two sources. We prove that usual separation methods based only on spatial diversity have performance limitations when the sources overlap in the time-frequency plane. We propose a new separation scheme combining spatial diversity and structured source priors. We present possible priors based on nonlinear Independent Subspace Analysis (ISA) and Hidden Markov Models (HMM), whose parameters are learnt on solo musical excerpts. We show with an example that they actually improve the separation performance.

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Author information

Authors and Affiliations

  1. IRCAM, Analysis-Synthesis Group, 1, place Igor Stravinsky, F-75004, Paris, France

    Emmanuel Vincent & Xavier Rodet

Authors
  1. Emmanuel Vincent
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  2. Xavier Rodet
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Vincent, E., Rodet, X. (2004). Underdetermined Source Separation with Structured Source Priors. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_42

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_42

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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