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.
A. Ghazimatin, E. Garmash, G. Penha, K. Sheets, M. Achenbach, O. Semerci, R. Galvez, M. Tannenberg, S. Mantravadi, D. Narayanan, O. Kalaydzhyan, D. Cole, B. Carterette, A. Clifton, P. N. Bennett, C. Hauff, M. Lalmas-Roelleke
Amar Ashar, Karim Ginena, Maria Cipollone, Renata Barreto, Henriette Cramer
Marco De Nadai, Francesco Fabbri, Paul Gigioli, Alice Wang, Ang Li, Fabrizio Silvestri, Laura Kim, Shawn Lin, Vladan Radosavljevic, Sandeep Ghael, David Nyhan, Hugues Bouchard, Mounia Lalmas-Roelleke, Andreas Damianou
