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

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.

► We consider the blind source separation problem in the noisy context.
► We propose a new methodology in order to enhance separation in terms of efficiency and robustness.
► The separation is obtained by minimizing divergence criteria combined with the total variation.
► The performance of the proposed algorithm has been illustrated through numerical simulations.