Learning Interpretable Musical Compositional Rules and Traces
This addresses the need for machines to emulate music theorists in analyzing and generating music, though it is incremental as it builds on existing feature learning methods.
The paper tackles the problem of automatically discovering interpretable compositional rules from symbolic music, presenting MUS-ROVER, which recovers known rules and identifies new patterns in Bach's chorales.
Throughout music history, theorists have identified and documented interpretable rules that capture the decisions of composers. This paper asks, "Can a machine behave like a music theorist?" It presents MUS-ROVER, a self-learning system for automatically discovering rules from symbolic music. MUS-ROVER performs feature learning via $n$-gram models to extract compositional rules --- statistical patterns over the resulting features. We evaluate MUS-ROVER on Bach's (SATB) chorales, demonstrating that it can recover known rules, as well as identify new, characteristic patterns for further study. We discuss how the extracted rules can be used in both machine and human composition.