Blind Source Separation Using Mixtures of Alpha-Stable Distributions

Nicolas Keriven 1 Antoine Deleforge 2, 3 Antoine Liutkus 4
2 PANAMA - Parcimonie et Nouveaux Algorithmes pour le Signal et la Modélisation Audio
Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
3 MULTISPEECH - Speech Modeling for Facilitating Oral-Based Communication
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
4 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We propose a new blind source separation algorithm based on mixtures of alpha-stable distributions. Complex symmetric alpha-stable distributions have been recently showed to better model audio signals in the time-frequency domain than classical Gaussian distributions thanks to their larger dynamic range. However, inference of these models is notoriously hard to perform because their probability density functions do not have a closed-form expression in general. Here, we introduce a novel method for estimating mixture of alpha-stable distributions based on characteristic function matching. We apply this to the blind estimation of binary masks in individual frequency bands from multichannel convolutive audio mixes. We show that the proposed method yields better separation performance than Gaussian-based binary-masking methods.
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https://hal.inria.fr/hal-01633215
Contributor : Nicolas Keriven <>
Submitted on : Friday, February 9, 2018 - 11:44:00 AM
Last modification on : Tuesday, April 2, 2019 - 2:15:54 PM
Document(s) archivé(s) le : Friday, May 4, 2018 - 3:57:31 AM

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Nicolas Keriven, Antoine Deleforge, Antoine Liutkus. Blind Source Separation Using Mixtures of Alpha-Stable Distributions. ICASSP 2018 - IEEE International Conference on Acoustics, Speech and Signal Processing, Apr 2018, Calgary, Canada. pp.771-775, ⟨10.1109/ICASSP.2018.8462095⟩. ⟨hal-01633215v3⟩

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