  In addressing the challenge of interpretability and generalizability of
artificial music intelligence, this paper 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. A comprehensive empirical
evaluation, contrasting this novel approach with existing methodologies,
manifests considerable advancements in interpretability and controllability.
This study marks a substantial contribution to the fields of music analysis,
composition, and computational musicology.
