A Computational Study of Patterned Melodic Structures across Musical Cultures

Research Overview

For more details, please also visit the project website.

Musical compositions are generally made up of highly-patterned structures—pitch, rhythm, timbre, dynamics, and so on. The study of such structures using computational models, especially machine-assisted analyses of large musical corpora, is a rapidly growing field. Yet such work has tended both to confine itself to single musical styles and to ignore the rich conceptual legacy of music theory.  Music theorists has developed sophisticated descriptions of pitch and other aspects of patterned musical structure.  However, these conceptualizations have rarely been examined empirically, especially with computation methods.  In other words, there is a gap between theoretical and empirical studies of musical structure.

This research team will help fill this gap by bringing a renewed theoretical attention to empirical studies of musical structure, focusing on the melody along with pitch, voice, and voice leading.  The team will also use advances in the empirical musical sciences to verify age-old, but untested, theories of musical structure.  Moreover, the focus on melodic structure gives the project a global scope, given the centrality of melody to most of the world’s musical cultures.  For this reason, the team will examine six collections (corpora) of the world’s music, from Indian and Irish melodies to the music of Bach and Mozart, thus also contribute to cross-cultural music theory.  The key statistical methodology includes a modeling framework known as topic models, as well as Bayesian hierarchical modeling.  The innovation of this project lies in the cross-cultural comparison of musical structure enabled by data science methods.  The results of the study will also have implications for classroom pedagogy and curricular design, and these implications extend beyond the field of music theory to performance practices as well.  The project may also help motivate the development of new modeling and algorithmic ideas that may prove useful for other types of richly structured data.  Through this project, the team will help U-M become a forerunner in the emerging field of empirical music theory.