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Updated: Jul 26, 2025

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Automated composition of Galician Xota-tuning RNN-based composers for specific musical styles using deep Q-learning.

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  • 1GLAM-Group on Language Audio & Music, Department of Computing, Imperial College London, London, United Kingdom.

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|June 22, 2023
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Summary

This study enhances AI music composition by training a Deep Q Network with custom rules to generate Galician Xota music. The approach aims to overcome common issues like repetition and lack of structure in AI-generated music.

Keywords:
Automated music compositionDeep Q-LearningGalician XotaMagentaRL Tuner

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Area of Science:

  • Artificial Intelligence
  • Music Technology
  • Computational Musicology

Background:

  • Automating music composition is challenging due to subjective aesthetic criteria.
  • Existing neural network models often produce repetitive and stylistically inconsistent music.
  • Previous AI music generation lacked adherence to specific musical genres and structures.

Purpose of the Study:

  • To extend the Magenta RL Tuner model for emulating the specific musical genre of the Galician Xota.
  • To develop a novel rule-set and reward functions for guiding AI music generation.
  • To improve the stylistic coherence and structural integrity of AI-composed music.

Main Methods:

  • Designing a genre-specific rule-set for the Galician Xota.
  • Implementing these rules as reward functions to train a Deep Q Network (DQN).
  • Utilizing the trained DQN to generate musical pieces adhering to the defined rules.

Main Results:

  • Successfully implemented a rule-set that effectively enforces stylistic and structural constraints on generated compositions.
  • Demonstrated a viable methodology for training AI models to emulate specific musical genres.
  • Achieved a significant improvement in the quality and consistency of AI-generated Galician Xota music.

Conclusions:

  • The developed rule-based training approach enhances AI music composition by enforcing stylistic and structural adherence.
  • This methodology provides a robust framework for future research in genre-specific AI music generation.
  • Future work can explore broader applications and refine the experimental procedures for improved model performance.