A Machine Learning Approach to Modality and Genre in Early Music

Daniel Tompkins, Florida State University

This paper presents a corpus study that identifies the number of distinct modes used in sacred and secular genres prior to 1650. Corpora used for the study include Masses, motets, and secular songs from the Franco-Flemish School, works by Palestrina, secular Italian songs with alfabeto guitar tablature from the early seventeenth century, and works by J.S. Bach. K-means clustering and key profiles will be used to determine the number of distinguishable modes in each corpus. The results of this study show that genre plays an essential role in determining the number of modes in a corpus, with secular genres being more likely to cluster into two modes and secular genres into several modes. This paper also explores the differences between systems of notation and musical practice and suggests other ways in which machine learning techniques can be in dialogue with the study of harmonic practice in early music.