Generalized method for solving the permutation problem in frequency-domain blind source separation of convolved speech signals

The blind speech separation of convolutive mixtures can be performed in the time-frequency domain. The separation problem becomes to a set of instantaneous mixing problems, one for each frequency bin, that can be solved independently by any appropiated instantaneous ICA algorithm. However, the arbitrary order of the estimated sources in each frequency, known as permutation problem, has to be solved to succesfully recover the original sources. This paper deals with the permutation problem in the general case of N sources and N observations. The proposed method combines a correlation approach based on the amplitude correlation property of speech signals, and an optimal pairing scheme to align the permuted solutions. Our method is robust to artificially permuted speech signals. Experimental results on simulated convolutive mixtures show the effectiveness of the proposed method in terms of quality of separated signals by objective and perceptually measures.