Creating latent spaces for modern music genre rhythms using minimal training data
Gabriel Vigliensoni, Louis McCallum, Rebecca Fiebrink · Goldsmiths Research Online · 2022
In this paper, we present R-VAE, a system designed for the exploration of latent spaces of musical rhythms. Unlike most previous work in rhythm modeling, R-VAE can be trained with small datasets, enabling rapid customization and exploration by individual users. R-VAE employs a data representation that encodes simple and compound meter rhythms. To the best of our knowledge, this is the first time that a network architecture has been used to encode rhythms with these characteristics, which are common in some modern popular music genres.