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 sho ws how hidden Markov models (HMMs) can be used to build an environment al noise recognition system based on a time-frequency analysis of the noise signal. The theory of HMMs is briefly reviewed in the context of automatic noise recognition. The performance of the HMM-based approac h is evaluated experimentally for the classification of five types of noise events (cal, truck, moped, aircraft, train). With more than 95% of correct classifications, the HMM-based approach is found to outperf orm previously proposed classifiers which were based on the average sp ectrum of noise events. A classification rest performed with human lis teners for the same data shows that the best HMM-based classifier also outperforms the ''average'' human listener who achieves only 91.8% of correct classifications for the same task. (C) 1998 Elsevier Science Ltd. All rights reserved.