2016/06/17 by Haizi Yu, Yu, Haizi, Lav R. Varshney +5 · 1 citation
Computer Science · Mathematics · Neuroscience · #Art #Artificial intelligence #Composition (language) #Computer science #FOS: Computer and information sciences #Feature (linguistics) #Linguistics #Literature #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Music Technology and Sound Studies #Music and Audio Processing #Musical #Musical composition #Natural language processing #Neuroscience and Music Perception #Visual arts #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1606.05572
published in arXiv (Cornell University) (Cornell University) · presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY
arxiv created 2016/06/17 · openalex publication_date 2016/06/17 · arxiv updated 2016/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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.