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.
Bibliographic reference. Ntalampiras, Stavros / Potamitis, Ilyas / Fakotakis, Nikos (2010): "Identification of abnormal audio events based on probabilistic novelty detection", In INTERSPEECH-2010, 2218-2221.