A robust algorithm for convolutive blind source separation in presence of noise

Authors:
M. El Rhabi;H. Fenniri;A. Keziou;E. Moreau
Affiliations:
LAMAI, FSTG, Université Cadi Ayyad-Marrakech, Morocco;CReSTIC, Université de Reims Champagne-Ardenne, France;Laboratoire de Mathématiques de Reims EA 4535, Université de Reims Champagne-Ardenne, France and Fédération ARC Mathématiques FR 3399 du CNRS, France;Aix Marseille Université, CNRS, ENSAM, LSIS, UMR 7296, 13397 Marseille, France, and Université de Toulon, CNRS, LSIS, UMR 7296, 83957 La Garde, France
Venue:
Signal Processing
Year:
2013

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Abstract

We consider the blind source separation (BSS) problem in the noisy context. We propose a new methodology in order to enhance separation performances in terms of efficiency and robustness. Our approach consists in denoising the observed signals through the minimization of their total variation, and then minimizing divergence separation criteria combined with the total variation of the estimated source signals. We show by the way that the method leads to some projection problems that are solved by means of projected gradient algorithms. The efficiency and robustness of the proposed algorithm using Hellinger divergence are illustrated and compared with the classical mutual information approach through numerical simulations.