An Inverse-Gamma Source Variance Prior with Factorized Parameterization for Audio Source Separation

Dionyssos Kounades-Bastian 1 Laurent Girin 1, 2 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 : In this paper we present a new statistical model for the power spectral density (PSD) of an audio signal and its application to multichannel audio source separation (MASS). The source signal is modeled with the local Gaussian model (LGM) and we propose to model its variance with an inverse-Gamma distribution, whose scale parameter is factorized as a rank-1 model. We discuss the interest of this approach and evaluate it in a MASS task with underdetermined convolutive mixtures. For this aim, we derive a variational EM algorithm for parameter estimation and source inference. The proposed model shows a benefit in source separation performance compared to a state-of-the-art LGM NMF-based technique.
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https://hal.inria.fr/hal-01253169
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Dionyssos Kounades-Bastian, Laurent Girin, Xavier Alameda-Pineda, Sharon Gannot, Radu Horaud. An Inverse-Gamma Source Variance Prior with Factorized Parameterization for Audio Source Separation. 41st IEEE International Conference on Acoustics, Speech and SIgnal Processing (ICASSP 2016), IEEE Signal Processing Society, Mar 2016, Shanghai, China. pp.136-140, ⟨10.1109/ICASSP.2016.7471652⟩. ⟨hal-01253169⟩

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