Degree Year
2017
Document Type
Thesis - Open Access
Degree Name
Bachelor of Arts
Department
Computer Science
Advisor(s)
Benjamin Kuperman
Adam Eck
Keywords
Machine learning, Computational creativity, Chorale harmonization, Neural networks
Abstract
Computational creativity researchers interested in applying machine learning to computer composition often use the music of J.S. Bach to train their systems. Working with Bach, though, requires grappling with the conventions of tonal music, which can be difficult for computer systems to learn. In this paper, we propose and implement an alternate approach to composition and harmonization of chorales based on pitch-relative note encodings to avoid tonality altogether. We then evaluate our approach using a survey and expert analysis, and find that pitch-relative encodings do not significantly affect human-comparability, likability or creativity. However, an extension of this model that better addresses the criteria survey participants used to evaluate the music, such as instrument timbre and harmonic dissonance, still shows promise.
Repository Citation
Goree, Samuel P., "Towards a Relative-Pitch Neural Network System for Chorale Composition and Harmonization" (2017). Honors Papers. 223.
https://digitalcommons.oberlin.edu/honors/223
