MusicAOG: An Energy-Based Model for Learning and Sampling a Hierarchical Representation of Symbolic Music

Abstract

In addressing the challenge of interpretability and generalizability of artificial music intelligence, this article introduces a novel symbolic representation that amalgamates both explicit and implicit musical information across diverse traditions and granularities. Utilizing a hierarchical and-or graph representation, the model employs nodes and edges to encapsulate a broad spectrum of musical elements, including structures, textures, rhythms, and harmonies. This hierarchical approach expands the representability across various scales of music. This representation serves as the foundation for an energy-based model, uniquely tailored to learn musical concepts through a flexible algorithm framework relying on the minimax entropy principle. Utilizing an adapted Metropolis–Hastings sampling technique, the model enables fine-grained control over music generation. Through a comprehensive empirical evaluation, this novel approach demonstrates significant improvements in interpretability and controllability compared to existing methodologies. This study marks a substantial contribution to the fields of music analysis, composition, and computational musicology.