A penalized mutual information criterion for blind separation of convolutive mixtures

The blind separation problem of linear time-dependent mixtures is addressed in this paper. We have developed a new algorithm based on the minimization of the mutual information as well as a penalized term which ensures an a priori normalization of the estimated sources (outputs) and so, avoids the scale indeterminacy. The criterion minimization is done using a well-known gradient approach. Finally, some numerical results are shown to illustrate the performance of the penalized algorithm compared to the Babaie-Zadeh approach presented in (Proceedings of IWANN, Granada, Spain, June 2001, pp. 834–842). r 2004 Elsevier B.V. All rights reserved.