Library learning with e-graphs on jazz harmony

TL;DR

This paper introduces a computational model that learns jazz harmonic patterns by discovering concise, generative explanations through library learning and e-graph-based program synthesis, mimicking human pattern internalization.

Contribution

It presents a novel approach combining library learning, e-graphs, and deductive parsing to model how humans learn and internalize complex musical patterns.

Findings

The model effectively captures harmonic pattern structures.

Libraries learned are intuitive and resemble human derivations.

The approach demonstrates promising alignment with human musical learning.

Abstract

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.