A sound source classification system based on subband processing
Oytun Türk, Ömer Şayli̇, Helin Dutağacı, Levent M. Arslan · 2002
A classification system that aims to recognize the presence of sounds from different sources is described. The type of audio signals considered are speech, music, noise and silence. Appropriate subband processing is applied for the characterization of each sound source. The algorithm operates in four steps to classify the contents of a given audio signal. The acoustical parameters and statistical measures to be used in the classification process are obtained via an off-line training procedure. In the silence and onset detection stages, we aim to label the starting and finishing instants of the acoustical events present in the audio signal. Acoustical parameters of the given signal are extracted, analysis of variance and classification using the LBG algorithm is carried out by generating codebooks of acoustical vectors. Experimental work is carded out on a database that contains mixtures of human speech, music, noise and silence. The experiments demonstrate that the system achieves 88% classification success on the average when sounds from different sources are presented non-simultaneously.