Library learning with e-graphs on jazz harmony
For researchers in music cognition and computational creativity, this work offers a novel approach to modeling human-like pattern learning in jazz harmony, but the results are preliminary and lack quantitative validation.
The paper presents a computational model that learns jazz harmonic patterns by searching over programs composed of primitive harmonic relations, integrating library learning with e-graphs to discover concise generative explanations. The model is evaluated on intuitiveness and similarity to human-written derivations, though no concrete performance numbers are provided.
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.