Abstract
In this chapter, we will be introducing a new efficient Blind Source Separation (BSS) method that handles mixtures of independent and dependent sources. In order to achieve that, we minimize a criterion based on Kullback-Lebiler divergence to set apart the observed mixtures of either dependent or independent sources using copulas as a tool to model this dependency. The proposed algorithm utilizes a gradient descent method to retrieve the signal sources. The efficiency and robustness of this approach is showcased through different experimental results.
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