SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder

DSpace/Manakin Repository

SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder

Citable link to this page

 

 
Title: SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder
Author: Nagler, Dylan Jeremy
Citation: Nagler, Dylan Jeremy. 2014. SCHUBOT: Machine Learning Tools for the Automated Analysis of Schubert’s Lieder. Bachelor's thesis, Harvard College.
Full Text & Related Files:
Abstract: This paper compares various methods for automated musical analysis, applying machine learning techniques to gain insight about the Lieder (art songs) of com- poser Franz Schubert (1797-1828). Known as a rule-breaking, individualistic, and adventurous composer, Schubert produced hundreds of emotionally-charged songs that have challenged music theorists to this day. The algorithms presented in this paper analyze the harmonies, melodies, and texts of these songs. This paper begins with an exploration of the relevant music theory and ma- chine learning algorithms (Chapter 1), alongside a general discussion of the place Schubert holds within the world of music theory. The focus is then turned to automated harmonic analysis and hierarchical decomposition of MusicXML data, presenting new algorithms for phrase-based analysis in the context of past research (Chapter 2). Melodic analysis is then discussed (Chapter 3), using unsupervised clustering methods as a complement to harmonic analyses. This paper then seeks to analyze the texts Schubert chose for his songs in the context of the songs’ relevant musical features (Chapter 4), combining natural language processing with feature extraction to pinpoint trends in Schubert’s career.
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:12705172
Downloads of this work:

Show full Dublin Core record

This item appears in the following Collection(s)

 
 

Search DASH


Advanced Search
 
 

Submitters