Classification of audio signals using statistical features on time and wavelet transform domains

T. Lambrou, P. Kudumakis, R. Speller, M. Sandler, A. Linney

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

87 Citations (Scopus)


This paper presents a study on musical signal classification, using wavelet transform analysis in conjunction with statistical pattern recognition techniques. A comparative evaluation between different wavelet analysis architectures in terms of their classification ability, as well as between different classifiers is carried out. We seek to establish which statistical measures clearly distinguish between the three different musical styles of rock, piano, and jazz. Our preliminary results suggest that the features collected by the adaptive splitting wavelet transform technique performed better compared to the other wavelet based techniques, achieving an overall classification accuracy of 91.67%, using either the minimum distance classifier or the least squares minimum distance classifier. Such a system can play a useful part in multimedia applications which require content based search, classification, and retrieval of audio signals, as defined in MPEG-7.
Original languageEnglish
Title of host publicationProceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing
PublisherIEEE Explore
Publication statusPublished - 1998


Dive into the research topics of 'Classification of audio signals using statistical features on time and wavelet transform domains'. Together they form a unique fingerprint.

Cite this