Sparsity-Based Algorithms for Blind Separation of Convolutive Mixtures with Application to EMG Signals

Abdelwaheb Boudjellal (1) , Karim Abed-Meraim (1) , Abdeldjalil Aissa El Bey (2, 3) , Adel Belouchrani (4) , Philippe Ravier (1)

1 PRISME - Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique
2 SC - Département Signal et Communications
3 Lab-STICC_TB_CACS_COM
4 ENP - École nationale polytechnique [Alger, Algérie]

In this paper we propose two iterative algorithms for the blind separation of convolutive mixtures of sparse signals. The first one, called Iterative Sparse Blind Separation (ISBS), minimizes a sparsity cost function using an approximate Newton technique. The second algorithm, referred to as Givens-based Sparse Blind Separation (GSBS) computes the separation matrix as a product of a whitening matrix and a unitary matrix estimated, via a Jacobi like process, as the product of Givens rotations which minimize the sparsity cost function. The two sparsity based algorithms show significantly improved performance with respect to the time coherence based SOBI algorithm as illustrated by the simulation results and comparative study provided at the end of the paper.

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

Abdelwaheb Boudjellal, Karim Abed-Meraim, Abdeldjalil Aissa El Bey, Adel Belouchrani, Philippe Ravier. Sparsity-Based Algorithms for Blind Separation of Convolutive Mixtures with Application to EMG Signals. IEEE Workshop on Statistical Signal Processing (SSP), Jun 2014, Gold Coast, Australia. Statistical Signal Processing Workshop (SSP), Australia, July 2014. ⟨hal-01002447⟩