Robust approach for blind separation of noisy mixtures of independent and dependent sources

Abstract

The framework of this article is to introduce a new efficient Blind Source Separation (BSS) method that handles mixtures of noise-contaminated independent / dependent sources. In order to achieve that, one can minimize a criterion that fuses a separating part, based on Kullback–Leibler divergence to set apart the observed mixtures of either dependent or independent sources, with a regularization part that employs the bilateral total variation (BTV) for the purpose of denoising the observations. The proposed algorithm utilizes a primal-dual algorithm to remove the noise, while a gradient descent method is implemented to retrieve the source signals. Our algorithm has shown its effectiveness and efficiency toward the noisy dependent / independent sources and also surpassed the standard BSS algorithms through different experimental results.