Date of Award

1-1-2018

Document Type

Thesis

Degree Name

M.S.

Department

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.

Provenance

Received from ProQuest

Rights holder

Andrew Hannum

File size

69 p.

File format

application/pdf

Language

en

Discipline

Computer science

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