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

Vue détaillée

Vue Refer

Vue Labintel

Vue BibTeX

%0 Conference Proceedings
%A Liuni, Marco
%A Roebel, Axel
%A Romito, Marco
%A Rodet, Xavier
%T Rényi Information Measures for Spectral Change Detection
%D 2011
%B ICASSP
%C Prague
%F Liuni11b
%K Change detection
%K spectral entropy
%K Kullback divergence
%K Rényi entropies
%K segmentation
%X Change detection within an audio stream is an important task in several domains, such as classification and segmentation of a sound or of a music piece, as well as indexing of broadcast news or surveillance applications. In this paper we propose two novel methods for spectral change detection without any assumption about the input sound: they are both based on the evaluation of information measures applied to a time-frequency representation of the signal, and in particular to the spectrogram. The class of measures we consider, the Rényi entropies, are obtained by extending the Shannon entropy definition: a biasing of the spectrogram coefficients is realized through the dependence of such measures on a parameter, which allows refined results compared to those obtained with standard divergences. These methods provide a low computational cost and are well-suited as a support for higher level analysis, segmentation and classification algorithms.
%1 6
%2 3
%U http://articles.ircam.fr/textes/Liuni11b/