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Composing Music Inspired by Sculpture: A Cross-Domain Mapping and Genetic Algorithm Approach
Francisco Braga1,2, Helena Sofia Pinto1,2
1INESC ID, 1000-029 Lisbon, Portugal.
This study explores using 3D sculpture models to compose music via cross-domain mapping and a Genetic Algorithm. The approach successfully generated musically interesting compositions, showing promise for modeling artistic inspiration.
Area of Science:
- Computational creativity
- Artificial intelligence in art
- Music composition
Background:
- Cross-domain mapping is explored as a method to model artistic inspiration.
- Current methods may not fully capture abstract connections between different art forms.
Purpose of the Study:
- To present a system for music composition based on 3D sculpture models.
- To test the hypothesis that cross-domain mapping can model inspiration abstractly.
Main Methods:
- Utilized a 3D sculpture model as input for music composition.
- Employed a Genetic Algorithm to enhance musical interest in the compositions.
- Focused on abstract feature mapping rather than direct semantic interpretation.
Main Results:
- The system generated compositions that participants rated highly (4/5 on average).
- Participants successfully related the generated music to the original sculptures.
- The abstract mapping approach yielded musically interesting results.
Conclusions:
- Cross-domain mapping using abstract features is a viable approach for modeling artistic inspiration.
- The developed system demonstrates a novel method for AI-assisted music composition.
- This work represents a step towards computational models of creative inspiration.
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