Measuring Dependence for Permutation Alignment in Convolutive Blind Source Separation

This brief proposes an effective implementation for addressing permutation ambiguity issue of convolutive blind source separation in frequency domain. Generally, signal envelope and power ratio as common inter-frequency dependence measures are utilized to group bin-wise separated signals for convolutive mixtures, where the new measure of permutation alignment method is represented by the activation matrix of bin-wise spectrum which is based on nonnegative matrix factorization (NMF). Meanwhile, canonical correlation analysis (CCA) rather than the maximum sum of correlation coefficient among different bins, is applied for verifying correlation determinations. In addition, the influence of bin distance and separation quality at each bin are explored to optimize permutation result. Simulation results demonstrate the effectiveness of the proposed technique in real recorded convolutive mixtures.

Automated summary

This paper proposes a new method for addressing permutation ambiguity issue of convolutive blind source separation in the frequency domain. The technique utilizes nonnegative matrix factorization (NMF) and canonical correlation analysis (CCA) to effectively separate and align the signals of convolutive mixtures.