Amour Keziou

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We consider the blind source separation (BSS) problem in the noisy context. We propose a new methodology in order to enhance separation performances in terms of efficiency and robustness. Our approach consists in denoising the observed signals through the minimization of their total variation, and then minimizing divergence separation criteria combined with(More)
We introduce a new BSS approach, based on modified Kullback-Leibler divergence between copula densities, for both independent or dependent source component signals. In the standard case of independent source components, the proposed method improves the mutual information (between probability densities) procedure, and it has the advantage to be naturally(More)
We introduce a new blind source separation approach, based on modified Kullback– Leibler divergence between copula densities, for both independent and dependent source component signals. In the classical case of independent source components, the proposed method generalizes the mutual information (between probability densities) procedure. Moreover, it has(More)
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