Alpha-Stable Multichannel Audio Source Separation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2017

Alpha-Stable Multichannel Audio Source Separation


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.
Fichier principal
Vignette du fichier
alphaStableMASS-ICASSP17.pdf (245.44 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01416366 , version 1 (11-01-2017)


  • HAL Id : hal-01416366 , version 1


Simon Leglaive, Umut Şimşekli, Antoine Liutkus, Roland Badeau, Gael Richard. Alpha-Stable Multichannel Audio Source Separation. 42nd International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Mar 2017, New Orleans, United States. ⟨hal-01416366⟩
562 View
626 Download


Gmail Facebook X LinkedIn More