A study on content-based classification and retrieval of audio database

Abstract

Nowadays, available audio corpora are rapidly increasing from fast growing Internet and digitized libraries. How to effectively classify and retrieve such huge databases is a challenging task. Content based technology is studied to automatically classify audio into hierarchy classes. Based on a small set of features selected by the sequential forward selection (SFS) method from 87 extracted ones, four classifiers, namely nearest neighbor (NN), modified k-nearest neighbor (k-NN), Gaussian mixture model (GMM), and probabilistic neural network (PNN) are compared. Experiments were conducted on a common database and a more comprehensive database built by the authors. Finally, the PNN classifier combined with Euclidean distance measurement was chosen for audio retrieval, using query by example.