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This paper introduces a library learning model for discovering concise generative explanations of jazz harmonic progressions. The model integrates deductive parsing with library learning on e-graphs to efficiently search the joint space of programs and libraries of harmonic patterns. Experiments demonstrate the model's ability to capture aspects of human musical pattern learning by evaluating the intuitiveness of learned programs and libraries and comparing them to human-written harmonic derivations.
E-graphs can help AI learn the unwritten rules of jazz harmony, mirroring how human musicians internalize complex musical patterns.
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.
