Spatial location priors for Gaussian model based reverberant audio source separationReport as inadecuate

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* Corresponding author 1 Technicolor Cesson Sévigné 2 PAROLE - Analysis, perception and recognition of speech Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery 3 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE

Abstract : We consider the Gaussian framework for reverberant audio source separation, where the sources are modeled in the time-frequency domain by their short-term power spectra and their spatial covariance matrices. We propose two alternative probabilistic priors over the spatial covariance matrices which are consistent with the theory of statistical room acoustics and we derive expectation-maximization algorithms for maximum a posteriori MAP estimation. We argue that these algorithms provide a statistically principled solution to the permutation problem and to the risk of overfitting resulting from conventional maximum likelihood ML estimation. We show experimentally that in a semi-informed scenario where the source positions and certain room characteristics are known, the MAP algorithms outperform their ML counterparts. This opens the way to rigorous statistical treatment of this family of models in other scenarios in the future.

Keywords : Gaussian Probabilistic priors Inverse-Wishart Audio source separation Spatial covariance EM algorithm

Author: Ngoc Duong - Emmanuel Vincent - Rémi Gribonval -



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