Affiliation:
1. Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, USA
2. Queen Mary University of London, London, UK
Abstract
Repetition and structure have a significant place in music theory, but the structure hierarchy and its influences are often ignored in both music analysis and music generation. In this article, we first describe novel algorithms based on repetition to extract music structure hierarchy from a MIDI data set of popular music and show its effectiveness through evaluation. Then, we introduce new data-driven approaches to estimate and validate structural influences in music. Results show that the automatically detected hierarchical repetition structures reveal significant interactions between structure and harmony, melody, rhythm, and predictivity. Different levels of hierarchy interact differently, providing evidence that structural hierarchy plays an important role in our popular music data set beyond simple notions of repetition or similarity. We further study how musical structure has evolved over decades of popular music writing. Finally, we discuss the importance of this work in highlighting roles that structure can play in music analysis, music similarity, music generation, music evaluation, and other music information retrieval tasks.