- Recherche et sélection de publications
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Alpha-Stable Multichannel Audio Source Separation
- Simon Leglaive #1, Umut Simsekli #1, Antoine Liutkus #2, Roland Badeau #1, Gaël Richard #1
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| #1 |
Laboratoire traitement et communication de l'information (LTCI)
- Télécm ParisTech
- Institut Mines-Télécom
- Université Paris-Saclay
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| #2 |
INRIA Nancy - Grand Est
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- References
- 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, USA, IEEE, March 2017,
- Abstract
In this paper, we focus on modeling multichannel audio signals in the short-time Fourier transform domain for the purpose of source separation. We propose a probabilistic model based on a class of heavy-tailed distributions, in which the observed mixtures and the latent sources are jointly modeled by using a certain class of multivariate alpha-stable distributions. As opposed to the conventional Gaussian models, where the observations are constrained to lie just within a few standard deviations near the mean, the pro- posed heavy-tailed model allows us to account for spurious data or important uncertainties in the model. We develop a Monte Carlo Expectation-Maximization algorithm for making inference in the proposed model. We show that our approach leads to significant improvements in audio source separation under corrupted mixtures and in spatial audio object coding.
- Keywords
- Alpha-stable distributions, Multichannel source separation, Informed source separation, Monte Carlo Expectation-Maximization
- Category
- Paper in proceedings
- Research Area(s)
- Engineering Sciences/Signal and Image processing
- Identifier(s)
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HAL ref. hal-01416366
Bibliographic key SL:ICASSP2-17
- File(s)
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- Last update
- on march 20, 2017 by Roland Badeau
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