Recherche
Panier électronique
Votre panier ne contient aucune notice
Connexion à la base
(Identifiez-vous pour accéder aux fonctions de mise à jour. Utilisez votre login-password de courrier électronique)
Entrepôt OAI-PMH
 | 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 |
(full translation not yet available)
Consultation des notices
| Vue détaillée | Vue Refer | Vue Labintel | Vue BibTeX |
|
%0 Generic %A Bietti, Alberto %T Online learning for audio clustering and segmentation %D 2014 %I ENS %F Bietti14a %X Audio segmentation is an essential problem in many audio signal processing tasks which tries to segment an audio signal into homogeneous chunks, or segments. Most current approaches rely on a change-point detection phase for finding segment boundaries, followed by a similarity matching phase which identifies similar segments. In this thesis, we focus instead on joint segmentation and clustering algorithms which solve both tasks simultaneously, through the use of unsupervised learning techniques in sequential models. Hidden Markov and semi-Markov models are a natural choice for this modeling task, and we present their use in the context of audio segmentation. We then explore the use of online learning techniques in sequential models and their application to real-time audio segmentation tasks. We present an existing online EM algorithm for hidden Markov models and extend it to hidden semi-Markov models by introducing a different parameterization of semi-Markov chains. Finally, we develop new online learning algorithms for sequential models based on incremental optimization of surrogate functions. %1 8 %2 1 %U http://architexte.ircam.fr/textes/Bietti14a/ |