Frequency-Domain Blind Separation of Convolutive Speech Mixtures with Energy Correlation-Based Permutation Correction

Abstract

Blind separation of convolutive speech mixtures in frequency domain has obvious advantages in term of convergence and computation, but suffers from permutation ambiguity. Motivated by the fact that speech signals have strong correlations across frequency, the paper presents an energy correlation method for solving permutation ambiguity after separation of instantaneous speech mixtures at each frequency bin. Extensive experiments with synthetic and recorded speech signals are carried out to compare the energy correlation method to amplitude correlation method, three different complex-valued independent component analysis (ICA) algorithms are compared as well. The results show that the proposed method achieves better performance than the amplitude correlation method, and the complex ICA algorithm based on negentropy maximization yields the best separation.