机器学习的象征性组成规则与遗传编程:不和的治疗在Palestrina
Torsten Anders1, Benjamin Inden2
1School of Media Arts and Performance, University of Bedfordshire, Luton, Bedfordshire, UK.
PeerJ. Computer science
|July 6, 2023
概括
这项研究介绍了一种使用基因编程 (GP) 来自动从 corpora. 提取音乐规则的新方法. 这种方法成功地模拟了Palestrina中的不和治疗方法.
科学领域:
- 计算音乐学 计算音乐学
- 音乐理论 音乐理论
- 人工智能的人工智能
背景情况:
- 算法组合需要符号规则.
- 从音乐体中提取这些规则是具有挑战性的.
- 遗传编程 (GP) 为规则提取提供了一个潜在的解决方案.
研究的目的:
- 开发和评估一种自动从音乐中提取象征性的作曲规则的方法.
- 将这种方法应用于模拟Palestrina音乐中的异调处理.
- 研究GP在音乐学习逻辑和数值关系方面的有效性.
主要方法:
- 开发了一种定制的算法,用于在音乐片段中标记异调.
- 使用 DBSCAN 来自动将旋律碎片集成到不和类别.
- 采用基因编程 (GP) 来学习基于积极和消极例子的异调治疗规则.
主要成果:
- 成功地从音乐体中提取了结合逻辑和数值关系的象征规则.
- 证明了GP学习特定异调类别规则的能力.
- 学习的规则是人类可以解释的,并且可以用于算法组成.
结论:
- 提出的方法有效地从音乐中提取象征性的作曲规则.
- 基因编程是一种学习复杂音乐模式的可行技术,特别是不和治疗.
- 提取的规则可以增强算法组合系统和音乐学分析.
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