Paper (AMR11): Combining Sources of Description for Approximating Music Similarity Ratings

This paper was presented at AMR 2011 in Barcelona. This is going to be published soon! Please contact the authors for a copy and further information.

Authors: Daniel Wolff and Tillman Weyde

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 a notable correlation between songs’ genres and associated similarity ratings, but learning on a combined feature set clearly outperforms either individual approach.

This entry was posted in research, tools and tagged . Bookmark the permalink.

Comments are closed.