Meta-features and AdaBoost for music classification (2006)
| Venue: | Machine Learning Journal : Special Issue on Machine Learning in Music |
| Citations: | 3 - 0 self |
BibTeX
@INPROCEEDINGS{Bergstra06meta-featuresand,
author = {James Bergstra and Balázs Kégl},
title = {Meta-features and AdaBoost for music classification},
booktitle = {Machine Learning Journal : Special Issue on Machine Learning in Music},
year = {2006}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Abstract. One of the biggest challenges facing current methods for classifying music by genre or artist is that features of the sound are computed on very small temporal scales (20 to 50 milliseconds), while the labels need to be assigned at relatively large temporal scales (3 to 5 minutes). We address this challenge by partitioning songs into smaller pieces and classifying each one separately. Our choice of features together with an AdaBoost.MH classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence that the method of partitioning songs is better than classifying either entire songs or individual features, using a variety of popular features and classifiers.
