New blind source separation method of independent/dependent sources

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 the great advantage to be naturally extensible to separate mixtures of dependent source components. Simulation results are presented showing the convergence and the efficiency of the proposed algorithms.

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