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.
https://hal.inria.fr/hal-00840366
Contributeur : Emmanuel Vincent
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Soumis le : mardi 2 juillet 2013 - 12:38:04
Dernière modification le : mercredi 16 mai 2018 - 11:24:07
Document(s) archivé(s) le : mercredi 5 avril 2017 - 06:03:21
Joachim Thiemann, Emmanuel Vincent. A fast EM algorithm for Gaussian model-based source separation. EUSIPCO - 21st European Signal Processing Conference - 2013, Sep 2013, Marrakech, Morocco. 2013. 〈hal-00840366〉
