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)


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.


Covariance Matrice Generalize Source Blind Source Separation Initial Source Blind Deconvolution 
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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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