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

: pp. 761–769
Received: May 23, 2021
Accepted: June 07, 2021
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco
LMA, FST Beni-Mellal, Sultan Moulay Slimane University, Beni-Mellal, Morocco
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco

In this paper, a new Blind Source Separation (BSS) method that handles mixtures of noisy independent / dependent sources is introduced.  We achieve that by  minimizing a criterion that fuses a separating part, based on Kullback–Leibler divergence for 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 signal sources.  Our algorithm has shown its effectiveness and efficiency and also surpassed the standard existing BSS algorithms.

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Mathematical Modeling and Computing, Vol. 8, No. 4, pp. 761–769 (2021)