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Underdetermined Instantaneous Audio Source Separation via Local Gaussian Modeling

  • Emmanuel Vincent
  • Simon Arberet
  • Rémi Gribonval
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5441)

Abstract

Underdetermined source separation is often carried out by modeling time-frequency source coefficients via a fixed sparse prior. This approach fails when the number of active sources in one time-frequency bin is larger than the number of channels or when active sources lie on both sides of an inactive source. In this article, we partially address these issues by modeling time-frequency source coefficients via Gaussian priors with free variances. We study the resulting maximum likelihood criterion and derive a fast non-iterative optimization algorithm that finds the global minimum. We show that this algorithm outperforms state-of-the-art approaches over stereo instantaneous speech mixtures.

Keywords

Global Minimum Nonzero Entry Active Source Source Separation Blind Source Separation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Emmanuel Vincent
    • 1
  • Simon Arberet
    • 1
  • Rémi Gribonval
    • 1
  1. 1.METISS GroupIRISA-INRIARennes CedexFrance

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