Uncovering affinity of artists to multiple genres from social behaviour data
Claudio Baccigalupo and I have a paper at ISMIR entitled Uncovering affinity of artists to multiple genres from social behaviour data. The paper details a project we worked on for the past year or so involving popular music listening activity from a pool of MusicStrands (MyStrands) users.
We provide not only the paper, but also the dataset and the code used in our analysis. All of this is available at the website we have set up for the project.
The main contribution of the project is an analysis and illustration of genres as “fuzzy sets” rather than boolean labels. Through a co-occurence analysis of hundreds of thousands of user playlists, a frequency based “affinity” metric is formed between artists and genres. This affinity metric is a more detailed expression of the style of a given artist’s music. The idea and awareness of predominant genres are a trivial part of any person’s understanding of the vast corpus of popular music. However, genres typically are used as boolean categorical labels. I.e. an artist is understood to be associated with only one given genre.
By expressing a connection to multiple genres through our affinity metric, a more detailed picture of the artist emerges. We give a lot more examples in the website, so be sure to check it out.