Factorial scaled hidden Markov model for polyphonic audio representation and source separation

Abstract : We present a new probabilistic model for polyphonic audio termed Factorial Scaled Hidden Markov Model (FS-HMM), which generalizes several existing models, notably the Gaussian scaled mixture model and the Itakura-Saito Nonnegative Matrix Factorization (NMF) model. We describe two expectation-maximization (EM) algorithms for maximum likelihood estimation, which differ by the choice of complete data set. The second EM algorithm, based on a reduced complete data set and multiplicative updates inspired from NMF methodology, exhibits much faster convergence. We consider the FS-HMM in different configurations for the difficult problem of speech / music separation from a single channel and report satisfying results.
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Communication dans un congrès
IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA'09), Oct 2009, Mohonk, NY, United States. 2009
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  • HAL Id : inria-00553336, version 1

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Alexey Ozerov, Cédric Févotte, Maurice Charbit. Factorial scaled hidden Markov model for polyphonic audio representation and source separation. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA'09), Oct 2009, Mohonk, NY, United States. 2009. 〈inria-00553336〉

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