A Variational EM Algorithm for the Separation of Moving Sound Sources

Dionyssos Kounades-Bastian 1 Laurent Girin 2, 1 Xavier Alameda-Pineda 3 Sharon Gannot 4, 1 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
GIPSA-DPC - Département Parole et Cognition
Abstract : This paper addresses the problem of separation of moving sound sources. We propose a probabilistic framework based on the complex Gaussian model combined with non-negative matrix factorization. The properties associated with moving sources are modeled using time-varying mixing filters described by a stochastic temporal process. We present a variational expectation-maximization (VEM) algorithm that employs a Kalman smoother to estimate the mixing filters. The sound sources are separated by means of Wiener filters, built from the estimators provided by the proposed VEM algorithm. Preliminary experiments with simulated data show that, while for static sources we obtain results comparable with the base-line method of Ozerov et al., in the case of moving source our method outperforms a piece-wise version of the baseline method.
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Dionyssos Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda, Sharon Gannot, Radu Horaud. A Variational EM Algorithm for the Separation of Moving Sound Sources. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA 2015), IEEE Signal Processing Society, Oct 2015, New Paltz, NY, United States. pp.1-5, ⟨10.1109/WASPAA.2015.7336936⟩. ⟨hal-01169764v2⟩



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