Date of Award
Daniel Felix Ritchie School of Engineering and Computer Science, Computer Science
Mario A. Lopez, Ph.D.
Artificial intelligence, Generative music, Jazz improvisation, Machine learning, Neural network, Recurrent neural network
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.
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Received from ProQuest
Hannum, Andrew, "RNN-Based Generation of Polyphonic Music and Jazz Improvisation" (2018). Electronic Theses and Dissertations. 1532.