Date on Master's Thesis/Doctoral Dissertation
5-2011
Document Type
Master's Thesis
Degree Name
M.S.
Department (Legacy)
Department of Physics
Committee Chair
Elmaghraby, Adel S.
Subject
Computer music; Electronic music; Music--Mathematics
Abstract
Computers and music have shared a rich history since the 1950s. Many languages and standards have been built around music. Yet even before the advent of the computer, music shared algorithmic ideas with mathematics which brought about many new styles over the centuries. Today's computers provide even more power, and with Intelligence algorithms, are able to create complex systems for generating art. Music is no exception, but very little has been done in generating music using such algorithms. Reinforcement Learning provides a means of learning good motions of chord progressions in music theory. Dmitri Tymoczko's Latent model for the underlying chord structure creates a mesh orbifoidal network capturing voice leading and surrounding chords. This presentation discusses experimentation in the latent model with a combination of the ideas taught in traditional Tonal Harmonic theory. Unlike David Cope's work in mimicking composer styles using machine learning, this approach attempts to tackle the problem head on through experimentation with Tymoczko's latent model for chords. Reinforcement Learning provides a means for learning this network and reward states in order to reach a terminal goal (taught in music theory as cadencing chords). Using Reinforcement Learning we are then able to use the reinforced model to generate chord progressions which have a tonal center (a center of gravity pulling the chords towards a certain pitch class). Further, a discussion of the implemented algorithm is also given.
Recommended Citation
Reese, Kristopher W., "Computationally generated music using reinforcement learning." (2011). Electronic Theses and Dissertations. Paper 1195.
https://doi.org/10.18297/etd/1195
