Blind separation of convolutive mixtures of non-stationary and temporally uncorrelated sources based on joint diagonalization

Authors:
Hicham Saylani;Shahram Hosseini;Yannick Deville
Affiliations:
Laboratoire d'Electronique, de Traitement du Signal et de Modéelisation Physique Faculté des Sciences, Université Ibnou Zohr, Agadir, Morocco;Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, UPS-CNRS-OMP, Toulouse, France;Institut de Recherche en Astrophysique et Planétologie, Université de Toulouse, UPS-CNRS-OMP, Toulouse, France
Venue:
ICISP'12 Proceedings of the 5th international conference on Image and Signal Processing
Year:
2012

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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.