University of Denver Digital Commons @ DU Electronic Theses and Dissertations Graduate Studies 1-1-2018 RNN-Based Generation of Polyphonic Music and Jazz Improvisation Andrew Hannum University of Denver Follow this and additional works at: https://digitalcommons.du.edu/etd Part of the Artificial Intelligence and Robotics Commons, and the Music Pedagogy Commons 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 This Thesis is brought to you for free and open access by the Graduate Studies at Digital Commons @ DU. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Digital Commons @ DU. For more information, please contact
[email protected],
[email protected]. RNN-based generation of polyphonic music and jazz improvisation A Thesis Presented to the Faculty of the Daniel Felix Ritchie School of Engineering and Computer Science In Partial Fulfillment of the Requirements for the Degree Master of Science by Andrew Hannum November 2018 Advisor: Mario A. Lopez c Copyright by Andrew Hannum 2018 All Rights Reserved Author: Andrew Hannum Title: RNN-based generation of polyphonic music and jazz improvisation Advisor: Mario A. Lopez Degree Date: November 2018 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.