Exploration of Music Collections with Audio Embeddings
Channel: MusicTechnologyGroup
Date: 2022-07-22

PhD thesis defense of Philip Tovstogan
July 12tht, 2022

Abstract:
Music recommendation systems (RecSys) are integral to modern music streaming services. While there is much research on many aspects of RecSys, there is not enough research on exploration and discovery that contributes to long-term user retention. After conducting an anonymous survey, we identify that the exploration and rediscovery of the personal collections in particular needs improvement.
To address this, we take advantage of music tags (genre, moods) and use deep auto-tagging systems to construct latent spaces. We investigate different architectures, datasets, layers, and projections and how they affect the perceived similarity of nearest neighbors. Finally, we present a novel web interface to visualize music collections using audio embeddings.
We evaluate the proposed solution via semi-structured user interviews and conclude that it provides an excellent alternative to existing solutions. We believe that the contributions of this work enable more research and industry solutions for music exploration and discovery.