Blind Source Separation Using Mixtures of Alpha-Stable Distributions

Nicolas Keriven 1 Antoine Deleforge 2 Antoine Liutkus 3
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 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 random moment 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.
Type de document :
Pré-publication, Document de travail
2017
Liste complète des métadonnées

https://hal.inria.fr/hal-01633215
Contributeur : Nicolas Keriven <>
Soumis le : mercredi 15 novembre 2017 - 09:25:27
Dernière modification le : jeudi 24 mai 2018 - 15:59:21
Document(s) archivé(s) le : vendredi 16 février 2018 - 12:58:11

Identifiants

  • HAL Id : hal-01633215, version 2
  • ARXIV : 1711.04460

Collections

Citation

Nicolas Keriven, Antoine Deleforge, Antoine Liutkus. Blind Source Separation Using Mixtures of Alpha-Stable Distributions. 2017. 〈hal-01633215v2〉

Partager

Métriques

Consultations de la notice

290

Téléchargements de fichiers

62