RNN-Based Generation of Polyphonic Music and Jazz Improvisation

<p>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 <em>char-rnn</em>. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.</p>

Name thesis:degree_name

M.S.

Level thesis:degree_level

Masters Thesis

Year dc:date.available

2018

Author dc:creator

• Hannum, Andrew

Contributors dc:contributor

• Mario A. Lopez, Ph.D.

• Artificial intelligence
• Generative music
• Jazz improvisation
• Machine learning
• Neural network
• Recurrent neural network
• Artificial Intelligence and Robotics
• Computer Sciences
• Music Pedagogy

Statement dc:rights

• <p>Copyright is held by the author. User is responsible for all copyright compliance.</p>

Language dc:language

en

Repository record dc:identifier

https://digitalcommons.du.edu/etd/1532

OAI identifier oai:identifier

oai:digitalcommons.du.edu:etd-2532