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.


October 2021 | CSCW

Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?

Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, Jennifer Thom

September 2021 | ECML-PKDD

Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty

Judith Bütepage, Lucas Maystre, Mounia Lalmas

May 2021 | CHI

Towards Fairness in Practice: A Practitioner-Oriented Rubric for Evaluating Fair ML Toolkits

Brianna Richardson, Jean Garcia-Gathright, Samuel F. Way, Jennifer Thom, Henriette Cramer