Music genre embeddings

Supervisor: Maria Astefanoaei
Semester: Fall 2021
Tags: scalable algorithms, hyperbolic embeddings, Python, Spotify data

Musical genres are inherently ambiguous and difficult to define. Even more so is the task of establishing how genres relate to one another. Yet, genre is perhaps the most common and effective way of describing musical experience. The number of possible genre classifications (e.g. Spotify has over 4000 genre tags, LastFM over 500,000 tags) has made the idea of manually creating music taxonomies obsolete. The aim of this project is to create hyperbolic embeddings to learn a general music taxonomy based on the co-occurrence of genres. The student will create a new dataset based on the One million songs data and will compare the hyperbolic embeddings with a previous implementation.