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.
|Titolo:||A multidomain approach for automatic home environmental sound classification|
|Parole Chiave:||computer audition; content-based audio recognition; MPEG-7 audio standard; wavelet packets|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||2010|
|Enti collegati al convegno:||Renesas Electronics Corporation|
Nuance Communications, Inc.
Appen Pty Ltd
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|