Hierarchical Symbolic Pop Music Generation with Graph Neural Networks Article Swipe
Wei Yang Bryan Lim
,
Jinhua Liang
,
Huan Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.08155
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2409.08155
Music is inherently made up of complex structures, and representing them as graphs helps to capture multiple levels of relationships. While music generation has been explored using various deep generation techniques, research on graph-related music generation is sparse. Earlier graph-based music generation worked only on generating melodies, and recent works to generate polyphonic music do not account for longer-term structure. In this paper, we explore a multi-graph approach to represent both the rhythmic patterns and phrase structure of Chinese pop music. Consequently, we propose a two-step approach that aims to generate polyphonic music with coherent rhythm and long-term structure. We train two Variational Auto-Encoder networks - one on a MIDI dataset to generate 4-bar phrases, and another on song structure labels to generate full song structure. Our work shows that the models are able to learn most of the structural nuances in the training dataset, including chord and pitch frequency distributions, and phrase attributes.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2409.08155
- https://arxiv.org/pdf/2409.08155
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403663925
All OpenAlex metadata
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https://openalex.org/W4403663925Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2409.08155Digital Object Identifier
- Title
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Hierarchical Symbolic Pop Music Generation with Graph Neural NetworksWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-09-12Full publication date if available
- Authors
-
Wei Yang Bryan Lim, Jinhua Liang, Huan ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2409.08155Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2409.08155Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2409.08155Direct OA link when available
- Concepts
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Computer science, Graph, The Symbolic, Artificial neural network, Theoretical computer science, Artificial intelligence, Psychology, PsychoanalysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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