The Improved Method for Solving Permutation Problem in Frequency Domain Blind Source Separation of Speech Signals

The signals of convolutive mixture in time-domain can be transformed to instantaneous mixtures in frequency-domain and complex-valued independent component analysis (CICA) can separate efficiently the signals of instantaneous mixture in each frequency bin. However, since CICA is calculated in each frequency bin independently, the permutation ambiguity becomes a serious problem. The permutation ambiguity of CICA in each frequency bin should be aligned so that a separated signal in the time-domain contains frequency components of the same source signal. The paper presents a novel and efficient approach for solving the permutation problem in frequency domain blind source separation of speech signals. The new algorithm models the frequency-domain separated signals by means of Teager energy correlation between neighboring bins for the detection of correct permutations. Experimental results show that the proposed algorithm can efficiently solve the permutation ambiguity problem in each frequency bin.