Multi-band multi-centroid clustering based permutation alignment for frequency-domain blind speech separation

• An improved clustering algorithm to calculate a global permutation reference.
• Can better exploit the inter-frequency dependency of speech signals.
• Can reduce block permutation errors effectively.
• Can improve robustness in challenging scenarios.

This paper investigates the permutation ambiguity problem in frequency-domain blind source separation and proposes a robust permutation alignment algorithm based on inter-frequency dependency, which is measured by the correlation coefficient between the time activity sequences of separated signals. To calculate a global reference for permutation alignment, a multi-band multi-centroid clustering algorithm is proposed where at first the permutation inside each subband is aligned with multi-centroid clustering and then the permutation among subbands is aligned sequentially. The multi-band scheme can reduce the dynamic range of the activity sequence and improve the efficiency of clustering, while the multi-centroid clustering scheme can improve the precision of the reference and reduce the risk of wrong permutation among subbands. The combination of two techniques enables to capture the variation of the time–frequency activity of a speech signal precisely, promising robust permutation alignment performance. Extensive experiments are carried out in different testing scenarios (up to reverberation time of 700 ms and 4×44×4 mixtures) to investigate the influence of two parameters, the number of subbands and the number of clustering-centroids, on the performance of the proposed algorithm. Comparison with existing permutation alignment algorithms proves that the proposed algorithm can improve the robustness in challenging scenarios and can reduce block permutation errors effectively.