Choice and adaptation of statistical models for single channel singing voice separation. Choix et adaptation de modèles statistiques pour la séparation de voix chantée à partir d’un seul microphone

Choice and adaptation of statistical models for single channel singing voice separation.

Choix et adaptation de modèles statistiques pour la séparation de voix chantée à partir d’un seul microphone

Alexey Ozerov Pierrick Philippe  Rémi Gribonval  Frédéric Bimbot 

Orange Labs, 4 rue du Clos Courtel, BP 91226, 35512 Cesson Sévigné cedex, France

IRISA (CNRS & INRIA) - projet METISS, Campus de Beaulieu, 35042 Rennes Cedex, France

Page: 
211-224
|
Received: 
12 January 2006
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

The problem of singing voice extraction from mono audio recordings, i.e.,one microphone separation of voice and music,is studied.The approach is based on a priori probabilistic models for two sources,more precisely on Gaussian Mixture Models (GMM).A method for model adaptation to the characteristics of the mixed sources is developed and a comparative study of different models and estimators is performed.We show that the adaptation of the model of music from the non-vocal parts of songs yields good results in realistic conditions.

Résumé

Le problème de l’extraction de la voix chantée dans des enregistrements musicaux monophoniques, c’est-à-dire la séparation voix / musique avec un seul capteur,est étudié. Les approches utilisées sont basées sur des modèles statistiques a priori des deux sources (musique et voix),notamment sur des Modèles de Mélange de Gaussiennes (MMG). Une méthode d’adaptation des modèles aux caractéristiques des sources mélangées est proposée,et une étude comparative des différents modèles et estimateurs est effectuée. Les résultats montrent que l’adaptation du modèle de musique sur les parties non-vocales des chansons permet d’obtenir de bonnes performances dans un cadre réaliste.

Keywords: 

Single channel source separation,singing voice,statistical models,Gaussian mixture models,adaptive Wiener filtering, models adaptation.

Mots clés

Séparation de sources avec un seul capteur,voix chantée,modèles statistiques,modèles de mélange de gaussiennes,filtrage de Wiener adaptatif,adaptation de modèles.

1. Introduction
2. Présentation Générale des Méthodes de Séparation
3. Méthodes de Séparation à Base de MMG
4. Mesures de Performance
5. Adaptation des Modèles et Choix de la Méthode de Séparation
6. Cadre Expérimental
7. Expérimentations et Résultats
8. Conclusions et Perspectives
  References

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