2022 Student Research Conference:
35th Annual Student Research Conference

A Data Analysis of the Music of Joseph Haydn


Madison N. Nguyen
Dr. Scott Alberts and Dr. Jan Miyake (Oberlin College and Conservatory), Faculty Mentors

This research project encompasses data analysis on the works of Joseph Haydn, an Austrian composer from the late 18th to early 19th century. The data set was created by Dr. Jan Miyake and contains information on a collection of 189 Haydn compositions that include piano trios, piano sonatas, and symphonies. The final product analyzes the data with visualizations, cluster modeling, and CART analysis. The main focus of the research is both the theme and the blunt form of Haydn's works. The research compares theme density, the percent of the measures in the movement that are in the theme of the piece, through visuals of each work. It also predicts the blunt form classification of each work using cluster and CART analysis. The analysis indicates that blunt form and theme density hold importance in classifying movements. 

Keywords: Haydn, Data Analysis, Data Visualization, Music

Topic(s):Statistics
Music

Presentation Type: Oral Presentation

Session: 301-5
Location: SUB Alumni Room
Time: 2:15

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