On convolutive Blind Source Separation in a noisy context and a total variation regularization
Résumé
We propose a new strategy for improving classical Blind Source Separation (BSS) methods. This strategy consists in denois-ing both the observed signal and the estimated source signal , and is based on the minimization of regularized criterion which takes into account the Total Variation of the signal. We prove by the way that the method leads to a projection problem which is solved by means of projected gradient algorithm. The effectiveness and the robustness of the proposed separating process are shown on numerical examples.
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