Blind speech separation using OPCA method

During recent decades, much attention has been given to the separation of mixed sources, in particular for the blind case where both the sources and the mixing process are unknown and only recordings of the mixtures are available. In several situations it is desirable to recover all sources from the recorded mixtures, or at least to segregate a particular source. Furthermore, it may be useful to identify the mixing process itself to reveal information about the physical mixing system. This paper deals with blind speech separation of instantaneous mixtures of two noisy speech signals. The separation criterion is based on oriented principal components analysis (OPCA) method. OPCA is a (second order) extension of standard principal component analysis (PCA) aiming at maximizing the power ratio of a pair of signals. It is shown that OPCA, preceded by almost arbitrary temporal filtering, can be used for blindly separating temporally signals from their linear instantaneous mixtures. The advantage over other second order techniques is the lack of the pre-whitening (or sphering) step. OPCA models proposed earlier are used in simulations to separate a number of artificial sources demonstrating the validity of the method.