2021 Student Research Conference:
34th Annual Student Research Conference

A Data Visualization of the Music of Haydn


Peyton Bell
Dr. Scott Alberts and Dr. Jan Miyake (Oberlin College and Conservatory), Faculty Mentors

This research project analyzes data about Joseph Haydn, an Austrian composer from the late 18th to early 19th century. The data set, created by Dr. Jan Miyake, contains information on a collection of 189 piano sonatas, piano trios, and symphonies from Haydn and the research website analyzes the data with visualizations, linear regression analysis, CART analysis, and cluster modeling. Visualizations are a large part of the research. The focus of the research is the theme in Haydn’s works and also the blunt form of the works. The theme density variable in the data is the percent of the measures in the movement that are in the theme. The research visualizes theme density and analyzes how it can be predicted in a model.The analysis shows a strong linear model for theme density in Haydn’s work and a lack of importance in blunt form in classifying and clustering movements.

Keywords: Haydn, Music, Data Analysis, Statistics, Visualization

Topic(s):Statistics
Music

Presentation Type: Face-to-Face Oral Presentation

Session: 102-5
Location: SUB Activities Room
Time: 9:30

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