Performance evaluation of two semi-blind source separation methods

Abstract

Blind source separation methods resort to very weak hypothesis concerning the source signals, as well as the mixing matrix. This paper verifies experimentally the performance improvement in two different source separation algorithms when some statistical knowledge about the mixing matrix is used. A natural way of inserting such information in source separation methods is to put them in a Bayesian framework. This approach presents immediate applications in digital communication and speech signal processing systems, among many others.