This article presents a multidomain approach which addresses the problem of automatic home environmental sound recognition. The proposed system will be part of a human activity monitoring system which will be based on heterogeneous sensors. This work concerns the audio classification component and its primary role is to detect anomalous sound events. We compare the discriminative capabilities of three feature sets (MFCC, MPEG-7 low level descriptors and a novel set based on wavelet packets) with respect to the classification of ten sound classes. These are combined with state of the art generative techniques (GMM and HMM) for estimating the density function of each class. The highest average recognition rate is 95.7% and is achieved by the vector formed by all the feature sets juxtaposed.
A multidomain approach for automatic home environmental sound classification / S. Ntalampiras, I. Potamitis, N. Fakotakis - In: INTERSPEECH 2010[s.l] : ISCA, 2010. - ISBN 9781617821233. - pp. 2210-2213 (( Intervento presentato al 11. convegno Annual Conference of the International Speech Communication Association tenutosi a Makuhari nel 2010.
A multidomain approach for automatic home environmental sound classification
S. Ntalampiras;
2010
Abstract
This article presents a multidomain approach which addresses the problem of automatic home environmental sound recognition. The proposed system will be part of a human activity monitoring system which will be based on heterogeneous sensors. This work concerns the audio classification component and its primary role is to detect anomalous sound events. We compare the discriminative capabilities of three feature sets (MFCC, MPEG-7 low level descriptors and a novel set based on wavelet packets) with respect to the classification of ten sound classes. These are combined with state of the art generative techniques (GMM and HMM) for estimating the density function of each class. The highest average recognition rate is 95.7% and is achieved by the vector formed by all the feature sets juxtaposed.File | Dimensione | Formato | |
---|---|---|---|
i10_2210.pdf
accesso riservato
Tipologia:
Publisher's version/PDF
Dimensione
102.52 kB
Formato
Adobe PDF
|
102.52 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.