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

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

MMC.
2021;
Volume 8, Number 4
: pp. 761–769
https://doi.org/10.23939/mmc2021.04.761
Received: May 23, 2021
Accepted: June 07, 2021

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

Authors:
  1. A. Ourdou
  2. A. Ghazdali
  3. A. Laghrib
  4. A. Metrane
1
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco
2
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco
3
LMA, FST Beni-Mellal, Sultan Moulay Slimane University, Beni-Mellal, Morocco
4
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.

blind source separation
noisy mixtures
dependent sources
bilateral total variation
Kullback–Leibler divergence
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