Volume 56, Number 4, July-August 2022
Recent developments of operations research and data sciences
|Page(s)||2999 - 3015|
|Published online||30 August 2022|
Blind source separation using hellinger divergence and copulas
LIPIM, ENSA Khouribga, Sultan Moulay Slimane University, Khouribga, Morocco
* Corresponding author: firstname.lastname@example.org
Accepted: 28 July 2022
Whenever there is a mixture of signals of any type, e.g. sounds, images or any other form of source signals, Blind Source Separation (BSS) is the method utilized to separate these signals from the observations. The separation is done without any prior knowledge about the mixing process nor the source signals. In literature multiple algorithms have been deployed for this particular problem, however most of them depends on Independent Component Analysis (ICA) and its variations assuming the statistical independence of the sources. In this paper, we develop a new algorithm improving the separation quality for both independent and dependent sources. Our algorithm used copulas to accurately model the dependency structure and the Hellinger divergence as a distance measure since it can convergence faster and it is robust against noisy source signals. Many simulations were conducted for various samples of sources to illustrate the superiority of our approach compared to other methods.
Mathematics Subject Classification: 62H05 / 92C55
Key words: Blind source separation / Hellinger divergence / copulas / dependent sources / noise-contaminated sources
© The authors. Published by EDP Sciences, ROADEF, SMAI 2022
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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