Abstract:
In this paper, an approach is built to
automatically detect acoustic events that are
produced in a meeting or lecture room environment.
Six audio classes are to be classified through this
approach. The classes considered are music, speech,
clapping, door slam, cough, and laughter. Several
events samples are collected from the Internet.
Support Vector Machines (SVMs) perform training
and testing the events classification on perceptual
and MFCC features set. A hierarchical clustering
scheme is used therefore the required number of
binary SVM classifier is also reduced. The system is
tested on different data sets and its effectiveness is
determined with classification accuracy on audio
event frames.
