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EvoComposer: An Evolutionary Algorithm for 4-Voice Music Compositions
R De Prisco1, G Zaccagnino2, R Zaccagnino3
1Dipartimento di Informatica, University of Salerno, Fisciano (SA), 84084, Italy robdep@unisa.it.
Evolutionary Computation
|October 11, 2019
Summary
This study introduces EvoComposer, an evolutionary algorithm for automatic music composition, specifically addressing the unfigured harmonization problem. EvoComposer generates high-quality harmonizations by balancing harmonic and melodic objectives, outperforming other methods.
Area of Science:
- Computational Intelligence
- Artificial Intelligence in Music
- Algorithmic Composition
Background:
- Evolutionary algorithms are effective for creative problem-solving, with applications in music composition.
- The unfigured harmonization problem involves creating multi-voice musical pieces with appropriate chords and melodic lines.
- Existing methods may not fully address the complexities of automatic music generation.
Purpose of the Study:
- To develop an evolutionary algorithm, EvoComposer, for automatic 4-voice music harmonization.
- To address both harmonic and melodic aspects in music composition.
- To create a novel, hybrid approach for algorithmic music generation.
Main Methods:
- Developed EvoComposer, a multiobjective evolutionary algorithm based on NSGA-II.
- Utilized a novel chromosome representation for handling harmonic and nonharmonic tones.
- Implemented specialized musical operators and a hybrid evaluation function based on Bach's music corpus.
Main Results:
- EvoComposer successfully composes music by optimizing harmonic and melodic objectives.
- The algorithm outperforms other metaheuristics in generating musically creative and performant solutions.
- EvoComposer is the first evolutionary algorithm specifically designed for the unfigured harmonization problem.
Conclusions:
- EvoComposer provides an effective, automatic solution for the unfigured harmonization problem.
- The approach demonstrates potential for application in other music composition tasks.
- This research advances the field of AI-driven music creation.
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