Analysis of analysis: Using machine learning to evaluate the importance of music parameters for Schenkerian analysis

作者：Kirlin Phillip B^*; Yust Jason

来源：Journal of Mathematics and Music, 2016, 10(2): 127-148.

DOI：10.1080/17459737.2016.1209588

• Schenkerian analysis
• machine learning
• harmony
• melody
• rhythm
• feature selection
• 68T05
• 68T10
• supervised learning
• sound and music computing

While criteria for Schenkerian analysis have been much discussed, such discussions have generally not been informed by data. Kirlin [Kirlin, PhillipB., 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.

• 出版日期2016-7