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Research on the Interaction of Genetic Algorithm in Assisted Composition.

Han Hu1

  • 1School of Architecture and Art, Central South University, Changsha, Hunan 410006, China.

Computational Intelligence and Neuroscience
|December 2, 2021
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Summary

This study introduces a novel genetic algorithm approach for computer-aided music composition, optimizing character representation for better musical style learning. The method effectively preserves rhythm by associating pitch and time value coding, enhancing algorithmic music creation.

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Area of Science:

  • Artificial Intelligence in Art
  • Algorithmic Music Composition
  • Computational Musicology

Background:

  • Computer-aided composition aims to formalize music creation, reducing human involvement using computers.
  • Understanding composer thought processes through AI is key to simulating artistic creation.
  • Traditional methods struggle with rhythm characterization, hindering AI's ability to learn musical styles.

Purpose of the Study:

  • To develop an optimized character representation for algorithmic music composition using a genetic algorithm.
  • To improve the ability of compositional networks to learn and replicate musical styles.
  • To address the limitations of independent pitch and duration encoding by incorporating rhythm.

Main Methods:

  • Feature extraction from MIDI files.
  • Application of a genetic algorithm with a sampling coding method for character representation optimization.
  • Associative coding of pitch and time values to represent rhythm.

Main Results:

  • The proposed genetic algorithm approach optimizes character representation for algorithmic music composition.
  • Associating pitch and time value coding effectively preserves musical rhythm and style.
  • Compositional networks demonstrate improved learning of musical style characteristics.

Conclusions:

  • The developed method enhances computer-aided composition by better capturing musical rhythm.
  • This approach facilitates more accurate learning of musical styles by AI models.
  • Optimized character representation is crucial for advancing algorithmic music creation.