Blind Speech Separation with GCC-NMF

Sean U.N. Wood, Jean Rouat

We introduce a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization (NMF) with spatial localization via the generalized cross correlation (GCC) method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, over time, according to their spatial origins. Separation quality is evaluated using publicly available data from the SiSEC signal separation evaluation campaign consisting of stereo recordings of 3 and 4 concurrent speakers in reverberant environments. Performance is quantified using perceptual and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We compare our approach with other NMF-based speech separation algorithms including unsupervised and semi-supervised approaches. GCC-NMF outperforms the unsupervised model-based approach that combines NMF with spatial covariance mixture models, and compares favourably to semi-supervised approaches that leverage prior knowledge and information, despite being purely unsupervised itself.

DOI: 10.21437/Interspeech.2016-1449

Cite as

Wood, S.U., Rouat, J. (2016) Blind Speech Separation with GCC-NMF. Proc. Interspeech 2016, 3329-3333.

author={Sean U.N. Wood and Jean Rouat},
title={Blind Speech Separation with GCC-NMF},
booktitle={Interspeech 2016},