Creating Music Using a Generative Adversarial Network
By Billard, Mitchell; Bishop, Robert; Elsisy, Moustafa; Graves, Laura; Dr. Kolokolova, Antonina; Nagisetty, Vineel; and Northcott, Zachary: all affiliated with Memorial University of Newfoundland
- Introduction
- Usage
- More Info
Our research aims to discover if successful AI image generative models can also be used to generate music. We plan to use a Deconvolutional Generative Adversarial Network, a particular type of Artificial Neural Network, in an attempt to generate Irish music.
We hope to use the distinctive structure of Irish tunes to make them suitable for GAN-based music generation. Our main idea is to regard an Irish melody as a fixed-size object with cross-references among its parts: “music as a picture” view. The two key components of this project are:
- Preprocessing:
- Creating a well-defined format for music encoding suitable for GANs
- Representing “vertical” dependencies in the tunes through a music as a picture view
- Modifying GANs via:
- Structuring strides and kernel size to take advantage of observed features in image forms
- Modifying training rate to avoid “Mode Collapse”
- The Preprocessing code is found in the
src/Generation/
directory - The Neural Network training code is found in the
src/Model/
directory - The Data used for preprocessing and training is found in the
Data/
directory - The code for our website is found in the
Site/
directory
- To reproduce the results shown in the report, please run the notebook file
Ensemble.ipynb
which is found in theDocumentation/
directory.