Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis. Issue 2 (3rd May 2016)

• Record Type: Journal Article
• Authors: Kirlin, Phillip B.; Yust, Jason
• Is Part Of: Journal of mathematics and music. Volume 10:Issue 2(2016)
• Page Start: 127
• Page End: 148
• Publication Date: 2016-05-03
• Subjects: Schenkerian analysis -- machine learning -- harmony -- melody -- rhythm -- feature selection; 68T05 -- 68T10; supervised learning -- sound and music computing; Music theory -- Periodicals; Music -- Mathematical models -- Periodicals; Music -- Mathematics -- Periodicals; 781.2
• DOI: 10.1080/17459737.2016.1209588
• Languages: English
• ISSNs: 1745-9737

Abstract

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.