Support vector machine active learning for music retrieval

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 melfrequency cepstral coefficients to perform best in precisionat-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. Keywords Support vector machines

• Type: Journal
• Year: 2006
• Where: MMS
• Authors: Michael I. Mandel, Graham E. Poliner, Daniel P. W. Ellis
• Keywords: Digital Music Collections, Flexible Music Similarity, MMS 2006, MMS 2007, Music Similarity