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Blind Separation of Instantaneous Mixtures of Independent/Dependent Sources

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Abstract

Blind Source Separation (BSS) has always been an active research field within the signal processing community; it is used to reconstruct primary source signals from their observed mixtures. Independent Component Analysis has been and is still used to solve the BSS problem; however, it is based on the mutual independence of the original source signals. In this paper, we propose to use Copulas to model the dependency structure between these signals, enabling the separation of dependent source components; we also deploy \(\alpha \)-divergence as our cost function to minimize, considering its superiority to handle noisy data as well as its ability to converge faster. We test our approach for various values of alpha and give a comparative study between the proposed methodology and other existing methods; this approach exhibited a higher quality performance and accuracy, especially when the value of \(\alpha \) is equal to \(\frac{1}{2}\), which is equivalent to the Hellinger divergence.

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Data Availability Statement

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request

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Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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AO, AG, AL and AM performed conceptualization, data curation, formal analysis, investigation, methodology, project administration, validation, roles/writing—original draft, and writing—review and editing.

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Correspondence to Abdelghani Ghazdali.

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Ourdou, A., Ghazdali, A., Laghrib, A. et al. Blind Separation of Instantaneous Mixtures of Independent/Dependent Sources. Circuits Syst Signal Process 40, 4428–4451 (2021). https://doi.org/10.1007/s00034-021-01672-2

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