Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
Learning a large scale vocal similarity embedding for music
This work describes an approach for modeling singing voice at scale by learning lowdimensional vocal embeddings from large collections of recorded music. We derive embeddings for different representations of the voice with genre labels. We evaluate on both objective (ranked retrieval) and subjective (perceptual evaluation) tasks. We conclude with a summary of our ongoing effort to crowdsource vocal style tags to refine our model.
Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming Sessions
Aaron Ng, Rishabh Mehrotra
Rishabh Mehrotra, Niannan Xue, Mounia Lalmas