Recherche
Recherche simple
Recherche avancée
Panier électronique
Votre panier ne contient aucune notice
Connexion à la base
Identification
(Identifiez-vous pour accéder aux fonctions de mise à jour. Utilisez votre login-password de courrier électronique)
Entrepôt OAI-PMH
Soumettre une requête
| Consulter la notice détaillée |
| Version complète en ligne |
| Version complète en ligne accessible uniquement depuis l'Ircam |
| Ajouter la notice au panier |
| Retirer la notice du panier |
English version
(full translation not yet available)
Liste complète des articles
|
Consultation des notices
%0 Book Section
%A Dessein, Arnaud
%A Cont, Arshia
%A Lemaitre, Guillaume
%T Real-time detection of overlapping sound events with non-negative matrix factorization
%D 2013
%E Nielsen, Frank and Bhatia, Rajendra
%B Matrix Information Geometry
%I Springer
%P 341-371
%F Dessein13c
%X In this paper, we investigate the problem of real-time detection of overlapping sound events by employing non-negative matrix factorization techniques. We consider a setup where audio streams arrive in real-time to the system and are decomposed onto a dictionary of event templates learned off-line prior to the decomposition. An important drawback of existing approaches in this context is the lack of controls on the decomposition. We propose and compare two provably convergent algorithms that address this issue, by controlling respectively the sparsity of the decomposition and the trade-off of the decomposition between the different frequency components. Sparsity regularization is considered in the framework of convex quadratic programming, while frequency compromise is introduced by employing the beta-divergence as a cost function. The two algorithms are evaluated on the multi-source detection tasks of polyphonic music transcription, drum transcription and environmental sound recognition. The obtained results show how the proposed approaches can improve detection in such applications, while maintaining low computational costs that are suitable for real-time.
%1 4
%2 3
|
|