Frequency Domain Blind Source Separation for Robot Audition Using a Parameterized Sparsity Criterion

Mounira Maazaoui (1) , Yves Grenier (1) , Karim Abed-Meraim (1)

1 TSI - Département Traitement du Signal et des Images

In this paper, we introduce a modified lp norm blind source separation criterion based on the source sparsity in the timefrequency domain. We study the effect of making the sparsity constraint harder through the optimization process, making the parameter p of the lp norm vary from 1 to nearly 0 according to a sigmoid function. The sigmoid introduces a smooth lp norm variation which avoids the divergence of the algorithm. We compared this algorithm to the regular l1 norm minimization and an ICA based one and we obtained promising results.

Mots clés: blind source separation; microphone array; sparsity criterion; l1; lp; sigmoid

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

Mounira Maazaoui, Yves Grenier, Karim Abed-Meraim. Frequency Domain Blind Source Separation for Robot Audition Using a Parameterized Sparsity Criterion. The European Signal Processing Conference (EUSIPCO-2011), Aug 2011, Spain. ⟨hal-00683450⟩