Abstract
In this work, we present Score MUsic Graph (SMUG)-Explain, a framework for generating and visualizing explanations of graph neural networks applied to arbitrary prediction tasks on musical scores. Our system allows the user to visualize the contribution of input notes (and note features) to the network output, directly in the context of the musical score. We provide an interactive interface based on the music notation engraving library Verovio. We showcase the usage of SMUG-Explain on the task of cadence detection in classical music. All code is available on this https URL.
| Original language | English |
|---|---|
| Title of host publication | Sound and Music Computing (SMC) |
| 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
- 101004 Biomathematics
- 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
JKU Focus areas
- Digital Transformation
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