Academic Commons

Articles

Support Vector Machine Active Learning for Music Retrieval

Mandel, Michael I.; Poliner, Graham E.; Ellis, Daniel P. W.

Searching and organizing growing digital music collections requires a computational model of music similarity. This paper describes a system for performing flexible music similarity queries using SVM active learning. We evaluated the success of our system by classifying 1210 pop songs according to mood and style (from an online music guide) and by the performing artist. In comparing a number of representations for songs, we found the statistics of mel-frequency cepstral coefficients to perform best in precision-at-20 comparisons. We also show that by choosing training examples intelligently, active learning requires half as many labeled examples to achieve the same accuracy as a standard scheme.

Files

More Information

Published In
Multimedia Systems
Publisher DOI
https://doi.org/10.1007/s00530-006-0032-2
Volume
12
Issue
1
Pages
3 - 13
Academic Units
Electrical Engineering
Academic Commons provides global access to research and scholarship produced at Columbia University, Barnard College, Teachers College, Union Theological Seminary and Jewish Theological Seminary. Academic Commons is managed by the Columbia University Libraries.