Simultaneous diagonalization algorithm for blind source separation based on sub-band filtered features

Blind source separation (BSS) has received increased attention in the signal processing literature. The goal of blind source separation is signal recovery from an unknown channel through the maximization tar minimization) of some independence criterion. In our previous work, we derived a generalized criterion(19) (simultaneous diagonalization of correlation matrices-SDOC) for blind source separation and explored the time-frequency structure(20) of nonstationary Signals like speech. In this paper we analyze first the identifiability of sources and apply subband filters for feature extraction to improve the BSS performance of the SDOC algorithm in the realistic but difficult situation when the background noise is not negligible.