This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by generic rules involving arbitrary separating functions. While the basic versions of ...
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This paper deals with the separation of two convolutively mixed signals. The proposed approach uses a recurrent structure adapted by generic rules involving arbitrary separating functions. While the basic versions of this approach were defined and analyzed in our companion paper (Charkani and Deville, 1999), two extensions are considered here. The first one is intended for possibly colored signals. In addition, the second one may be used even when the probability density functions of the sources are unknown. We first analyze the convergence properties of these extended approaches at the separating state, i.e. we derive their equilibrium and stability conditions and their asymptotic error variance. We then determine the separating functions which minimize this error variance. We also report experimental results obtained in various conditions, ranging from synthetic data to mixtures of speech signals measured in real situations. These results confirm the validity of the proposed approaches and show that they significantly outperform classical source separation methods in the considered conditions. (C) 1999 Elsevier Science B.V. All rights reserved.
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