Blind adaptive principal eigenvector beamforming for acoustical source separation

For separating multiple speech signals given a convolutive mixture, time-frequency sparseness of the speech sources can be exploited. In this paper we present a multi-channel source separation method based on the concept of approximate disjoint orthogonality of speech signals. Unlike binary masking of singlechannel signals as e.g. applied in the DUET algorithm we use a likelihood mask to control the adaptation of blind principal eigenvector beamformers. Furthermore orthogonal projection of the adapted beamformer filters leads to mutually orthogonal filter coefficients thus enhancing the demixing performance. Experimental results in terms of the achievable signalto-interference ratio (SIR) and a perceptual speech quality measure are given for the proposed method and are compared to the DUET algorithm.