A Permutation Algorithm of Frequency-Domain Blind Source Separation Based on Influence Weights

Abstract

In the frequency domain, convolutive mixes with blind source separation can be successfully resolved. But the permutation issue in frequency-domain blind source separation needs to be resolved. We investigated the impact of frequency space and separation performance at each frequency bin on the amplitude correlation permutation algorithm with the goal of addressing the permutation ambiguity problem in frequency-domain blind source separation of convolutive mixtures, and we proposed an enhanced permutation algorithm. The improved algorithm uses spacing influence weight and performance influence weight to control the influence of the frequency bins sorted in the neighborhood on the frequency bins unsorted. Experiments have shown that the two influence weights are effective. Finally, blind source separation experiments are performed on the speech signals under the two convolutive mixing models and the simulated room mixing model. According to experiments, the increased signal to interference plus noise ratio of separated signals demonstrates that the improved algorithm outperforms the amplitude correlation permutation algorithm in terms of separation performance and robustness.