DSP seminar

DATE: Tuesday, January 27
TIME: 4 p.m.
LOCATION: Beckman Room 5602

SPEAKER: Dr. Christophe Couvreur

TITLE: 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
communication shows 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 event. 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.