Content-based audio classification and retrieval using the nearest feature line method

Abstract

A method is presented for content-based audio classification and retrieval. It is based on a new pattern classification method called the nearest feature line (NFL). In the NFL, information provided by multiple prototypes per class is explored. This contrasts to the nearest neighbor (NN) classification in which the query is compared to each prototype individually. Regarding audio representation, perceptual and cepstral features and their combinations are considered. Extensive experiments are performed to compare various classification methods and feature sets. The results show that the NFL-based method produces consistently better results than the NN-based and other methods. A system resulting from this work has achieved the error rate of 9.78%, as compared to that of 18.34% of a compelling existing system, as tested on a common audio database.