Complex nonconvex lp norm minimization for underdetermined source separation

Emmanuel Vincent 1
1 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Underdetermined source separation methods often rely on the assumption that the time-frequency source coefficients are independent and Laplacian distributed. In this article, we extend these methods by assuming that these coefficients follow a generalized Gaussian prior with shape parameter p. We study mathematical and experimental properties of the resulting complex nonconvex lp norm optimization problem in a particular case and derive an efficient global optimization algorithm. We show that the best separation performance for three-source stereo convolutive speech mixtures is achieved for small p.
Type de document :
Communication dans un congrès
7th Int. Conf. on Independent Component Analysis and Blind Source Separation (ICA), Sep 2007, London, United Kingdom. pp.430--437, 2007
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00544203
Contributeur : Emmanuel Vincent <>
Soumis le : mardi 7 décembre 2010 - 14:54:24
Dernière modification le : mercredi 16 mai 2018 - 11:23:03
Document(s) archivé(s) le : mardi 8 mars 2011 - 04:28:58

Fichier

vincent_ICA07bis.pdf
Fichiers éditeurs autorisés sur une archive ouverte

Identifiants

  • HAL Id : inria-00544203, version 1

Citation

Emmanuel Vincent. Complex nonconvex lp norm minimization for underdetermined source separation. 7th Int. Conf. on Independent Component Analysis and Blind Source Separation (ICA), Sep 2007, London, United Kingdom. pp.430--437, 2007. 〈inria-00544203〉

Partager

Métriques

Consultations de la notice

303

Téléchargements de fichiers

492