基于RNN的作曲家为特定的音乐风格自动创作加利西亚Xota调,使用深度Q学习
Rodrigo Mira1, Eduardo Coutinho2, Emilia Parada-Cabaleiro3
1GLAM-Group on Language Audio & Music, Department of Computing, Imperial College London, London, United Kingdom.
PeerJ. Computer science
|June 22, 2023
概括
这项研究通过训练Deep Q网络使用自定义规则来生成加利西亚Xota音乐来增强AI音乐组成. 这种方法旨在克服常见的问题,如人工智能生成的音乐中的重复和缺乏结构.
科学领域:
- 人工智能的人工智能
- 音乐 技术 音乐 技术
- 计算音乐学 计算音乐学
背景情况:
- 由于主观的美学标准,自动化音乐创作具有挑战性.
- 现有的神经网络模型往往会产生重复性和风格不一致的音乐.
- 之前的AI音乐世代缺乏对特定音乐类型和结构的坚持.
研究的目的:
- 扩展Magenta RL调器模型,以模拟加利西亚Xota的特定音乐类型.
- 开发一套新的规则和奖励功能,用于引导AI音乐生成.
- 提高人工智能组成的音乐的风格连贯性和结构完整性.
主要方法:
- 为加利西亚的Xota设计一种特定的规则集.
- 实施这些规则作为奖励功能来训练深度Q网络 (DQN).
- 使用训练好的DQN生成符合定义规则的音乐作品.
主要成果:
- 成功实施了一套规则,有效地强制执行对生成的作品的风格和结构限制.
- 展示了训练人工智能模型模拟特定音乐类型的可行方法.
- 在人工智能生成的加利西亚Xota音乐的质量和一致性方面取得了显著的改善.
结论:
- 开发的基于规则的培训方法通过强制执行风格和结构的坚持来增强AI音乐组成.
- 这种方法为未来关于特定类型AI音乐生成的研究提供了坚实的框架.
- 未来的工作可以探索更广泛的应用,并改进实验程序以提高模型性能.
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