Abstract

In this paper, we compare the effectiveness of basic acoustic features and genre annotations when adapting a music similarity model to user ratings. We use the Metric Learning to Rank algorithm to learn a Mahalanobis metric from comparative similarity ratings in in the MagnaTagATune database. Using common formats for feature data, our approach can easily be transferred to other existing databases. Our results show that genre data allow more effective learning of a metric than simple audio features, but a combination of both feature sets clearly outperforms either individual set.

Publication Type:	Book Section
Subjects:	M Music and Books on Music > M Music Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Departments:	School of Mathematics, Computer Science & Engineering > Computer Science
URI:	http://openaccess.city.ac.uk/id/eprint/2471

Preview

PDF Download (246kB) | Preview

Export

Downloads