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hal-00482287, version 1

Blind Spectral-GMM Estimation for Underdetermined Instantaneous Audio Source Separation

Simon Arberet 1, Alexey Ozerov () 2, Rémi Gribonval () a, 1, Frédéric Bimbot () 1

International Conference on Independent Component Analysis and Blind Source Separation (ICA) (2009) pp. 751 - 758

Résumé : The underdetermined blind audio source separation problem is often addressed in the time-frequency domain by assuming that each time-frequency point is an independently distributed random variable. Other approaches which are not blind assume a more structured model, like the Spectral Gaussian Mixture Models (Spectral-GMMs), thus exploiting statistical diversity of audio sources in the separation process. However, in this last approach, Spectral-GMMs are supposed to be learned from some training signals. In this paper, we propose a new approach for learning Spectral-GMMs of the sources without the need of using training signals. The proposed blind method significantly outperforms state-of-the-art approaches on stereophonic instantaneous music mixtures.

  • a –  INRIA
  • 1 :  METISS (INRIA - IRISA)
  • CNRS : UMR6074 – INRIA – Institut National des Sciences Appliquées (INSA) - Rennes – Université de Rennes 1
  • 2 :  Laboratoire Traitement et Communication de l'Information [Paris] (LTCI)
  • Télécom ParisTech – CNRS : UMR5141
  • Domaine : Sciences de l'ingénieur/Traitement du signal et de l'image
    Informatique/Traitement du signal et de l'image
  • Mots-clés : blind audio source separation – Spectral Gaussian Mixture Models – training signals
 
  • hal-00482287, version 1
  • http://hal.archives-ouvertes.fr/hal-00482287
  • oai:hal.archives-ouvertes.fr:hal-00482287
  • Contributeur : Alexey Ozerov <>
  • Soumis le : Lundi 10 Mai 2010, 10:50:27
  • Dernière modification le : Vendredi 7 Janvier 2011, 14:12:28