Blind Separation of Convolved Sources Based on Information Maximization

Blind separation of independent sources from their convolutive mixtures is a problem in many real world multi-sensor applications. In this paper we present a solution to this problem based on the information maximization principle, which was recently proposed by Bell and Sejnowski for the case of blind separation of instantaneous mixtures. We present a feedback network architecture capable of coping with convolutive mixtures, and we derive the adaptation equations for the adaptive lters in the network by maximizing the information transferred through the network. Examples using speech signals are presented to illustrate the algorithm.