Degree Year

2017

Document Type

Thesis

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.

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