An Unified Approach for Blind Source Separation Using Sparsity and Decorrelation

Fangchen Feng (1) , Matthieu Kowalski (2, 1)

1 L2S - Laboratoire des signaux et systèmes
2 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

Independent component analysis (ICA) has been a major tool for blind source separation (BSS). Both theoretical and practical evaluations showed that the hypothesis of independence suits well for audio signals. In the last few years, optimization approach based on sparsity has emerged as another efficient implement for BSS. This paper starts from introducing some new BSS methods that take advantages of both decorrelation (which is a direct consequence of independence) and sparsity using overcomplete Gabor representation. It is shown that the proposed methods work in both under-determined and over-determined cases. Experimental results illustrate the good performances of these approaches for audio mixtures.

Mots clés: Sparsity; Independant Component Analysis; Optimization

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

Fangchen Feng, Matthieu Kowalski. An Unified Approach for Blind Source Separation Using Sparsity and Decorrelation. 23rd European Signal Processing Conference (EUSIPCO 2015), Aug 2015, Nice, France. ⟨hal-01199620⟩