SCHUBOT
Subtitle: machine learning tools for the automated analysis of Schubert's Lieder
By: by Dylan J. Nagler ; submitted in partial fulfillment of the requirements for an AB degree with honors in Computer Science and Music
Contributors: Harvard School of Engineering and Applied Sciences; Harvard University. Department of Music
Published: 2014
Language: eng
Number of pages: 42
Subjects: Criticism and interpretation; Songs; Analysis, appreciation
Other titles: Machine learning tools for the automated analysis of Schubert's Lieder
Description: Combines various methods for automated musical analysis, applying machine learning techniques to gain insight about the Lieder of composer Franz Schubert. The algorithms presented in this paper analyze the harmonies, melodies, and texts of these songs. The paper begins with an exploration of the relevant music theory and machine learning algorithms and a discussion of Schubert's place in the world of music theory. The paper then focuses on automated harmonic analysis and hierarchical decomposition of MusicXML data, followed by melodic analysis using unsupervised clustering methods. The paper then analyzes the song texts in context of the relevant musical features, combining natural language processing with feature extraction to pinpoint trends in Schubert's career.
Notes: Cover title.

A.B. Harvard University 2014.