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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VigliensoniMcCallumFiebrink_ICCC2020.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (1MB) | Preview |
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) |
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Departments, Centres and Research Units: |
Computing |
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Dates: |
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Event Location: |
Coimbra, Portugal |
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Date range: |
7 – 11 September 2020 |
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Item ID: |
29044 |
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Date Deposited: |
17 Jul 2020 11:09 |
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Last Modified: |
22 Jun 2021 05:21 |
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URI: |
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