Date of Award
1-1-2018
Document Type
Masters Thesis
Degree Name
M.S.
Organizational Unit
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
First Advisor
Mario A. Lopez, Ph.D.
Keywords
Artificial intelligence, Generative music, Jazz improvisation, Machine learning, Neural network, Recurrent neural network
Abstract
This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.
Publication Statement
Copyright is held by the author. User is responsible for all copyright compliance.
Rights Holder
Andrew Hannum
Provenance
Received from ProQuest
File Format
application/pdf
Language
en
File Size
69 p.
Recommended Citation
Hannum, Andrew, "RNN-Based Generation of Polyphonic Music and Jazz Improvisation" (2018). Electronic Theses and Dissertations. 1532.
https://digitalcommons.du.edu/etd/1532
Copyright date
2018
Discipline
Computer science