Date of Award
4-6-2022
Document Type
Honors Thesis
First Advisor
Rodney Summerscales
Abstract
Advances in Recurrent Neural Network (RNN) techniques have caused an explosion of problems posed that revolve around the mass analysis and generation of sequential data, including symbolic music. Building off the work of Nathaniel Patterson’s Musical Autocomplete: An LSTM Approach, we extend this problem of continuing a composition by examining the creative impact that injecting latent-space encoded image data, specifically fine art from the WikiArt Dataset, has on the musical output of RNN architectures designed for autocomplete. For comparison purposes with Patterson, we will also be using a corpus of Erik Satie’s piano music for training, validation, and testing.
Recommended Citation
Braithwaite, Tyler, "Art to Influence Creativity in Algorithmic Composition" (2022). Honors Theses. 261.
https://digitalcommons.andrews.edu/honors/261
Subject Area
Computer algorithms; Machine learning; Artificial intelligence; Neural networks (Computer science); Computer music
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.