Spatial location priors for Gaussian model based reverberant audio source separation
Identifieur interne : 004676 ( Hal/Curation ); précédent : 004675; suivant : 004677Spatial location priors for Gaussian model based reverberant audio source separation
Auteurs : Ngoc Q. K. Duong [France] ; Emmanuel Vincent [France] ; Rémi Gribonval [France]Source :
- EURASIP Journal on Advances in Signal Processing [ 1687-6172 ] ; 2013.
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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.
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DOI: 10.1016/j.sigpro.2011.10.007
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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.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Spatial location priors for Gaussian model based reverberant audio source separation</title><author role="aut"><persName><forename type="first">Ngoc Q. 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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.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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