Learning Interpretable Musical Compositional Rules and Traces

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 Bachs (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.