Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis
Phillip B. Kirlin, Jason Yust · Journal of Mathematics and Music · 2016
While criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, Phillip B., 2014 Kirlin, Phillip B. 2014a. “A Data Set for Computational Studies of Schenkerian Analysis.” In Proceedings of the 15th International Society for Music Information Retrieval Conference, 213–218. [Google Scholar] “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 Yust, Jason. 2006. “Formal Models of Prolongation.” Ph.D. thesis, University of Washington, Seattle. [Google Scholar], 2006 Yust, Jason. 2006. “Formal Models of Prolongation.” Ph.D. thesis, University of Washington, Seattle. [Google Scholar] “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.