用程序库学习爵士和声，模拟人类反复聆听的内化过程
Library learning with e-graphs on jazz harmony
- 基于程序库搜索和声模式，用基础和声关系生成简洁解释
- 联合学习和声模式库与重构程序，提升对音乐结构的理解
- 适合音乐理论研究者和生成模型开发者参考
人类能形成高度结构化的音乐模式直觉理解，但通常需多次反思和重听才能完全内化。为捕捉这一内化过程，我们提出一种基于库学习的爵士和声模式计算模型。给定一组和声进行语料，该模型在由基础和声关系组成的程序空间中搜索，以发现语料的简洁生成解释。模型先为每首曲子枚举可能的程序，再联合学习一个和声模式库与重构后的程序。为高效探索程序与库的庞大联合空间，我们结合演绎解析与库学习，在e-graph上实现。通过评估程序与库的直观性，以及与人类编写的和声推导的相似性，我们探讨了模型在捕捉人类音乐模式学习方面的表现。
原文摘要 · Abstract (English)
Humans can acquire a highly structured intuitive understanding of musical patterns, yet these patterns often require multiple iterations of reflection and re-listening to internalize fully. To capture such an internalization process, we present a computational model for the learning of jazz harmonic patterns based on library learning. Given a corpus of harmonic progressions, our model searches over a space of programs composed of primitive harmonic relations in order to discover concise generative explanations of the corpus. The model first enumerates possible programs for each piece, and then jointly learns a library of harmonic patterns and refactored programs. To efficiently navigate the vast joint space of programs and libraries, we integrate deductive parsing with library learning on e-graphs. We explore how well our model captures aspects of human musical pattern learning by evaluating the intuitiveness of both programs and libraries, as well as similarities to human-written harmonic derivations.
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