Date of Submission
Spring 2016
Academic Programs and Concentrations
Computer Science
Project Advisor 1
Keith O'Hara
Abstract/Artist's Statement
The goal of this project was to use neural networks as a tool for live music performance. Specifically, the intention was to adapt a preexisting neural network code library to work in Max, a visual programming language commonly used to create instruments and effects for electronic music and audio processing. This was done using ConvNetJS, a JavaScript library created by Andrej Karpathy.
Several neural network models were trained using a range of different training data, including music from various genres. The resulting neural network-based instruments were used to play brief pieces of music, which they used as input to create unique musical output.
Max, while useful for live performance and audio processing, proved to be somewhat impractical for this project. Implementing too complex of a network caused performance issues and even crashing. Because of this, smaller networks, which are less robust in their prediction abilities had to be used, producing very simplistic musical patterns.
Access Agreement
Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Recommended Citation
Risdon, Daniel Wilton, "Algorithmic Music Composition and Accompaniment Using Neural Networks" (2016). Senior Projects Spring 2016. 352.
https://digitalcommons.bard.edu/senproj_s2016/352
This work is protected by a Creative Commons license. Any use not permitted under that license is prohibited.
Included in
Artificial Intelligence and Robotics Commons, Composition Commons, Other Computer Sciences Commons, Other Music Commons, Software Engineering Commons