A Variational EM Algorithm for the Separation of Time-Varying Convolutive Audio Mixtures

Dionyssos Kounades-Bastian 1 Laurent Girin 2, 1 Xavier Alameda-Pineda 3 Sharon Gannot 4 Radu Horaud 1
1 PERCEPTION - Interpretation and Modelling of Images and Videos
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
2 GIPSA-CRISSP - CRISSP
GIPSA-DPC - Département Parole et Cognition
Abstract : This paper addresses the problem of separating audio sources from time-varying convolutive mixtures. We propose a probabilistic framework based on the local complex-Gaussian model combined with non-negative matrix factorization. The time-varying mixing filters are modeled by a continuous temporal stochastic process. We present a variational expectation-maximization (VEM) algorithm that employs a Kalman smoother to estimate the time-varying mixing matrix, and that jointly estimate the source parameters. The sound sources are then separated by Wiener filters constructed with the estimators provided by the VEM algorithm. Extensive experiments on simulated data show that the proposed method outperforms a block-wise version of a state-of-the-art baseline method.
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https://hal.inria.fr/hal-01301762
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Submitted on : Tuesday, April 12, 2016 - 6:09:47 PM
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Dionyssos Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda, Sharon Gannot, Radu Horaud. A Variational EM Algorithm for the Separation of Time-Varying Convolutive Audio Mixtures. IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2016, 24 (8), pp.1408-1423. ⟨10.1109/TASLP.2016.2554286⟩. ⟨hal-01301762⟩

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