A single stage approach to blind source extraction based on second order statistics

A single stage blind source extraction (BSE) approach to extract one desired source signal from an instantaneous mixture of several signals is studied in this paper. In contrast to methods based on blind source separation (BSS) followed by a classifier we extract directly the desired source signal. In recent papers it has been shown that prior information about the desired source's mixing column or autocorrelation function can be incorporated in a single stage BSE algorithm. The focus of this paper is to give insight in the BSE problem for scenarios where joint diagonalization of correlation matrices is applicable, i.e., to exploit the full potential of the SOS of observed signals. In this work we unify and expand these previously presented BSE methods, which lead to the unified instantaneous blind source extraction (UIBSE) algorithm. We show that UIBSE is even more flexible than previously presented since both types of prior information may be combined to obtain better results in selecting the desired source. Above that, the same algorithm may be used for different objective functions. Finally, a performance comparison with BSS algorithms that are followed by a classifier shows that UIBSE is more robust to noisy observations