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Interactive music composition driven by feature evolution.

Maximos A Kaliakatsos-Papakostas1, Andreas Floros2, Michael N Vrahatis3

  • 1Department of Music, Aristotle University of Thessaloniki, 57001 Thessaloníki, Greece.

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|July 8, 2016
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Summary

This study introduces a novel two-level framework for evolutionary music composition, combining user interaction with feature-based generation. The system demonstrates a robust approach to interactive music creation, exploring convergence behavior with artificial raters.

Keywords:
Evolutionary music compositionFeature-based music compositionGenetic algorithmsInteractive music compositionParticle swarm optimisation

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Area of Science:

  • Computational creativity
  • Artificial intelligence in music
  • Evolutionary computation

Background:

  • Evolutionary algorithms are effective for automatic music generation.
  • Current methods include feature-based and interactive composition.
  • A hybrid approach can leverage the strengths of both.

Purpose of the Study:

  • To introduce a novel two-level methodological framework for evolutionary music composition.
  • To combine feature-based and interactive composition approaches.
  • To objectively examine the convergence behavior of this hybrid technique.

Main Methods:

  • A two-level system: higher level uses particle swarm optimization (PSO) for user-rated feature evolution, lower level uses a genetic algorithm (GA) for feature-based composition.
  • Utilized artificial raters to guide the system and analyze convergence characteristics.
  • Focused on examining convergence speed and the 'interestingness' of the compositional trajectory.

Main Results:

  • The proposed framework shows a fruitful and robust approach to interactive music composition.
  • Artificial raters enabled objective exploration of convergence characteristics.
  • The system's convergence behavior was analyzed in terms of optimality, speed, and creativity.

Conclusions:

  • The hybrid evolutionary music composition framework is a novel and effective approach.
  • The use of artificial raters provides a valuable tool for analyzing evolutionary systems.
  • This methodology offers a robust foundation for future research in interactive music generation.