The automatic classification of environmental noise sources from their acoustic signatures recorded at the microphone of a noise monitoring system (NMS) i:; an active subject of research nowadays. This paper shows how hidden Markov models (HMM's) can be used to build an environmental noise recognition system based on a timefrequency analysis of the noise signal. The performance of the proposed HMM-based approach is evaluated experimentally for the classification of five types of noise events (car, truck, moped, aircraft, train). The HMM-'based approach is found to outperform previously proposed classifiers based on the average spectrum of noise event with more than 95% of correct classifications. For comparison, a classification test is performed with human listeners for the same data which shows [.hat the best HMM-based classifier outperforms the "average" hu:man listener who achieves only 91 3% of correct classification for the same task.
Gaunard et al. REF have implemented a HMM-based classifier to recognise five noise events (car, truck, moped, aircraft, train).
Automatic classification of environmental noise events by hidden Markov models
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)
Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181)
Computer Science