Abstract
Graph Neural Networks (GNNs) have recently gained traction in symbolic music tasks, yet a lack of a unified framework impedes progress. Addressing this gap, we present GraphMuse, a graph processing framework and library that facilitates efficient music graph processing and GNN training for symbolic music tasks. Central to our contribution is a new neighbor sampling technique specifically targeted toward meaningful behavior in musical scores. Additionally, GraphMuse integrates hierarchical modeling elements that augment the expressivity and capabilities of graph networks for musical tasks. Experiments with two specific musical prediction tasks -- pitch spelling and cadence detection -- demonstrate significant performance improvement over previous methods. Our hope is that GraphMuse will lead to a boost in, and standardization of, symbolic music processing based on graph representations. The library is available at this https URL
| Original language | English |
|---|---|
| Title of host publication | International Society for Music Information Retrieval Conference (ISMIR) |
| Number of pages | 6 |
| Publication status | Published - 2024 |
Fields of science
- 202002 Audiovisual media
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102015 Information systems
- 101019 Stochastics
- 103029 Statistical physics
- 101018 Statistics
- 101017 Game theory
- 202017 Embedded systems
- 101016 Optimisation
- 101015 Operations research
- 101014 Numerical mathematics
- 101029 Mathematical statistics
- 101028 Mathematical modelling
- 101026 Time series analysis
- 101024 Probability theory
- 102032 Computational intelligence
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 101027 Dynamical systems
- 305907 Medical statistics
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- 305905 Medical informatics
- 101031 Approximation theory
- 102033 Data mining
- 305901 Computer-aided diagnosis and therapy
- 102019 Machine learning
- 106007 Biostatistics
- 102018 Artificial neural networks
- 106005 Bioinformatics
- 202037 Signal processing
- 202036 Sensor systems
- 202035 Robotics
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