Article details

Research area
Speech recognition

Applied Acoustics, vol. 54, no. 3, pp. 167-182


Christophe Couvreur, Vincent Fontaine, Pierre Gaunard, Corine-Ginette Mubikangiey

Automatic Classification of Environmental Noise Events by Hidden Markov Models


The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) is an active subject of research nowadays. This paper show how hidden Markov models (HMM’s) can be used to build an environmental noise recognition system based on a time-frequency analysis of the noise signal. The theory of HMM’s is briefly reviewed in the context of automatic noise recognition. The performance of the HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train). With more than 95\% of correct classifications, the HMM-based approach is found to outperform previously proposed classifiers which were based on the average spectrum of noise events. A classification test performed with human listeners for the same data shows that the best HMM-based classifier also outperforms the “average” human listener who achieves only 91.8\% of correct classification for the same task.

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