A variational EM algorithm for the separation of moving sound sources

D. Kounades-Bastian, X L. Girin, Alameda-Pineda, S. Gannot, R. P. Horaud

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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 baseline method [1], in the case of moving source our method outperforms a piece-wise version of the baseline method.
Original languageAmerican English
Title of host publicationIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
StatePublished - 2015

Bibliographical note

Place of conference:), New Paltz, USA


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