Analysis of analysis: importance of different musical parameters for Schenkerian analysis
Files
Accepted manuscript
Date
2017-04-04
Authors
Kirlin, Phillip B.
Yust, Jason
Version
Accepted manuscript
OA Version
Citation
Phillip B. Kirlin & Jason Yust (2016) Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis, Journal of Mathematics and Music, 10:2, pp. 127-148. https://doi.org/10.1080/17459737.2016.1209588
Abstract
While criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, Phillip B., 2014 “A Probabilistic Model of Hierarchical Music Analysis.” Ph.D. thesis, University of Massachusetts Amherst] has begun to fill this vacuum with a corpus of textbook Schenkerian analyses encoded using data structures suggested byYust [Yust, Jason, 2006 “Formal Models of Prolongation.” Ph.D. thesis, University of Washington] and a machine learning algorithm based on this dataset that can produce analyses with a reasonable degree of accuracy. In this work, we examine what musical features (scale degree, harmony, metrical weight) are most significant in the performance of Kirlin's algorithm.