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A fast EM algorithm for Gaussian model-based source separation
Abstract : We consider the FASST framework for audio source separation, which models the sources by full-rank spatial covariance matrices and multilevel nonnegative matrix factorization (NMF) spectra. The computational cost of the expectation-maximization (EM) algorithm in [1] greatly increases with the number of channels. We present alternative EM updates using discrete hidden variables which exhibit a smaller cost. We evaluate the results on mixtures of speech and real-world environmental noise taken from our DEMAND database. The proposed algorithm is several orders of magnitude faster and it provides better separation quality for two-channel mixtures in low input signal-to-noise ratio (iSNR) conditions.
Cited literature [13 references]
https://hal.inria.fr/hal-00840366
Contributor : Emmanuel Vincent <>
Submitted on : Tuesday, July 2, 2013 - 12:38:04 PM
Last modification on : Friday, March 6, 2020 - 1:20:41 AM
Document(s) archivé(s) le : Wednesday, April 5, 2017 - 6:03:21 AM
Contributor : Emmanuel Vincent <>
Submitted on : Tuesday, July 2, 2013 - 12:38:04 PM
Last modification on : Friday, March 6, 2020 - 1:20:41 AM
Document(s) archivé(s) le : Wednesday, April 5, 2017 - 6:03:21 AM
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thiemann_EUSIPCO13.pdf
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