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Blind Separation of Convolutive Mixtures of Non-stationary and Temporally Uncorrelated Sources Based on Joint Diagonalization

  • Hicham Saylani
  • Shahram Hosseini
  • Yannick Deville
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)

Abstract

In this paper, we propose a new method for blindly separating convolutive mixtures of non-stationary and temporally uncorrelated sources. It estimates each source and its delayed versions up to a scale factor by Jointly Diagonalizing a set of covariance matrices in the frequency domain, contrary to most existing second-order methods which require a Block Joint Diagonalization algorithm followed by a blind deconvolution to achieve the same result. Consequently, our method is much faster than these classical methods especially for higer-order mixing filters and may lead to better performance as confirmed by our simulation results.

Keywords

Covariance Matrice Generalize Source Blind Source Separation Initial Source Blind Deconvolution 
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 2012

Authors and Affiliations

  • Hicham Saylani
    • 1
  • Shahram Hosseini
    • 2
  • Yannick Deville
    • 2
  1. 1.Laboratoire d’Electronique, de Traitement du Signal et de Modéelisation Physique Faculté des SciencesUniversité Ibnou ZohrAgadirMorocco
  2. 2.Institut de Recherche en Astrophysique et PlanétologieUniversité de Toulouse, UPS-CNRS-OMPToulouseFrance

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