Document Type



The generation of music artificially is an interesting concept to many and has received a lot of attention in recent years. The advancement of neural networks has allowed for the creation of models that can seemingly generate music creatively to mimic a specific genre or composer. This project delved deep into the many ways to construct music generating neural networks and compared different model architectures and data engineering techniques. Three main types of models were implemented and the resulting generated music was evaluated with respect to the melody, note agreeableness, and rhythm. These models used the Bach Chorales corpus as inspiration for music generation.



Thesis Comittee

Dr. Wanchunzi Yu, Thesis Advisor
Dr. Kevin Rion, Committee Member
Dr. Nguyenho Ho, Committee Member

Copyright and Permissions

Original document was submitted as an Honors Program requirement. Copyright is held by the author.