Paper (Ismir11): Adapting Metrics for Music Similarity using Comparative Ratings

This paper was presented at the Ismir 2011 in Miami. The paper is available in the online proceedings as pdf

Authors: Daniel Wolff and Tillman Weyde

Abstract: Understanding how we relate and compare pieces of music has been a topic of great interest in musicology as well as for business applications, such as music recommender systems. The way music is compared seems to vary among both individuals and cultures. Adapting a generic model to user ratings is useful for personalisation and can help to better understand such differences. This paper presents an approach to use machine learning techniques for analysing user data that speci?es song similarity. We explore the potential for learning generalisable similarity measures with two state of-the-art algorithms for learning metrics. We use the audio clips and user ratings in the MagnaTagATune dataset, enriched with genre annotations from the Magnatune label.

PDF: Poster

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