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Non-Negative Matrix Factorization Applied to Auditory Scenes Classification

Type

text
 

Genre(s)

mémoire ou thèse
 

Forme(s)

document numérique
 

Cette ressource est disponible chez l'organisme suivant : Ircam - Centre Pompidou

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Titre

Non-Negative Matrix Factorization Applied to Auditory Scenes Classification
 

Nom(s)

Cauchi, Benjamin (auteur)
 

Publication

UPMC , 2011
 

Description

Sujet(s)

auditory scene analysis   sparseness   nonnegative matrix factorization
 

Résumé

This master's thesis is dedicated to the automatic classification of auditory scene using non-negative matrix factorization. A particular attention is paid to the performances achieved by the non-negative matrix factorization in sound sources detection. Our intuition was that a good classification could be achieve if we could efficiently detect the sources within auditory scenes. It appears on short artificial examples that taking into account the non-stationarity of the spectral content of the sound sources improves the source detection. Finally, our classification method is applied to a corpus of soundscapes of train stations and the results are compared with previous classifications methods. We finally conclude that using non-negative matrix factorization significantly improves the classification.
 

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Date de la notice

2012-02-09 01:00:00
 

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