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](loupe.gif) | Consulter la notice détaillée |
![Version complète en ligne](article.gif) | Version complète en ligne |
![Version complète en ligne accessible uniquement depuis l'Ircam](article_ionly.gif) | Version complète en ligne accessible uniquement depuis l'Ircam |
![Ajouter la notice au panier](panier_in.gif) | Ajouter la notice au panier |
![Retirer la notice du panier](panier_out.gif) | Retirer la notice du panier |
English version
(full translation not yet available)
Liste complète des articles
|
Consultation des notices
%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/
|
|