Assigning and Visualizing Music Genres by Web-based Co-Occurrence Analysis

Abstract We explore a simple, web-based method for predicting the genre of a given artist based on co-occurrence analysis, i.e. analyzing co-occurrences of artist and genre names on music-related web pages. To this end, we use the page counts provided by Google to estimate the relatedness of an arbitrary artist to each of a set of genres. We investigate four different query schemes for obtaining the page counts and two different probabilistic approaches for predicting the genre of a given artist. Evaluation is performed on two test collections, a large one with a quite general genre taxonomy and a quite small one with rather specific genres. Since our approach yields estimates for the relatedness of an artist to every genre of a given genre set, we can derive genre distributions which incorporate information about artists that cannot be assigned a single genre. This allows us to overcome the inflexible artist-genre assignment usually used in music information systems. We present a simple method to visualize such genre distributions with our Traveller’s Sound Player. Finally, we briefly outline how to adapt the presented approach to extract other properties of music artists from the web.