NDLI: Classification de signaux audio en temps-réel par un modèle de mélanges d'histogrammes

Content Provider	Semantic Scholar
Author	Baelde, Maxime Biernacki, Christophe Greff, Raphaël
Copyright Year	2017
Abstract	La reconnaissance sonore consiste a attribuer un label a un signal audio inconnu. Celle-ci repose generalement sur des descripteurs audio ainsi que des modeles d'apprentissage statistique. Neanmoins les modeles actuels peinent a bien classer les sons dans un contexte temps-reel ou ces derniers sont heterogenes. Ce papier propose une nouvelle methode basee sur un modele de melanges d'histogrammes representant les spectres audio. La reconnaissance consiste a calculer la probabilite de chaque groupe puis a les agreger temporellement. Une etape de reduction du precedent modele permet par ailleurs de passer au temps-reel. Cette methode surpasse les algorithmes actuels, et peut atteindre 96,7% de bonne classification sur une base de 50 classes de sons en utilisant 0,5s de donnees audio.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://hal.archives-ouvertes.fr/hal-01592496/file/Article%20JDS%202017.pdf
Alternate Webpage(s)	https://hal.archives-ouvertes.fr/hal-01592496/document
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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