Audio signal feature extraction and classification using local discriminant bases

Automatic classification of audio signals is an interesting and a challenging task. With the rapid growth of multimedia content over Internet, intelligent content-based audio and video retrieval techniques are required to perform efficient search over vast databases. Classification schemes form the basis of such content-based retrieval systems. In this paper we propose an audio classification scheme using local discriminant bases (LDB) algorithm. The audio signals were decomposed using wavelet packets and the high discriminatory nodes were selected using the LDB algorithm. Two different dissimilarity measures were used to select the LDB nodes and to extract features from them. The features were fed to a linear discriminant analysis based classifier for a six group (Rock, Classical, Country, Folk, Jazz and Pop) and a four group (Rock, Classical, Country and Folk) classifications. Overall classification accuracies as high as 77% and 88% were achieved for the six and four group classifications respectively using a database of 170 audio signals.