Over-complete blind source separation by applying sparse decomposition and information theoretic based probabilistic approach

Both in the case of cellular communication and in the case of spoken dialogue based information retrieval systems on mobile platforms there exist a number of interference signals: Therefore, it is essential to separate these interference signals from the intended signal(s) in order to have clear communication in the case of cellular phones and to improve the speech recognition accuracy in the case of spoken dialogue based information retrieval system. Since the number and nature of source signals (intended + interference signals) change, it is not practical to know them a priori. Therefore, it is not always practical to apply signal separation techniques that work well when the number of source signals is equal to the number of sensors. In addition, since how the signals get mixed is unknown, we need to apply blind techniques for the separation. This paper is concerned with a blind source separation (BSS) technique for the over-complete case (#signals > #sensors) that is based on the sparse decomposition and, the joint estimation of mixing matrix and the separated source signals by applying information theoretic based probabilistic approach. Experimental results of signal separation using various real speech and noise signals indicate that the quality of separated source signals are 4 dB better than the current techniques