Creating Latent Spaces for Modern Music Genre Rhythms Using Minimal Training Data

Vigliensoni, Gabriel; McCallum, Louis and Fiebrink, Rebecca. 2020. 'Creating Latent Spaces for Modern Music Genre Rhythms Using Minimal Training Data'. In: International Conference on Computational Creativity (ICCC). Coimbra, Portugal 7 – 11 September 2020. [Conference or Workshop Item]

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Abstract or Description

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.

Item Type:

Conference or Workshop Item (Paper)

Departments, Centres and Research Units:

Computing > Embodied AudioVisual Interaction Group (EAVI)


30 June 2020Accepted

Event Location:

Coimbra, Portugal

Date range:

7 – 11 September 2020

Item ID:


Date Deposited:

17 Jul 2020 11:09

Last Modified:

22 Jun 2021 05:21


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