Support vector machine active learning for music retrieval

被引：61
|
作者：
Mandel, Michael I. [1 ]
Poliner, Graham E. [1 ]
Ellis, Daniel P. W. [1 ]
机构：
[1] Columbia Univ, Dept Elect Engn, New York, NY 10027 USA
来源：
MULTIMEDIA SYSTEMS | 2006年 / 12卷 / 01期
关键词：
support vector machines; active learning; music classification;
D O I：
10.1007/s00530-006-0032-2
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：3 / 13
页数：11
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