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Activity Number: 319 - SLDS CSpeed 6
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318561
Title: Exploring Neural Networks' Ability to Generate Music
Author(s): NOAH Daniel SOLOMON* and Wanchunzi Yu
Companies: Bridgewater State University and Bridgewater State University
Keywords: Deep Learning; Neural Networks; LSTM; Artificial Intelligence
Abstract:

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 these 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.


Authors who are presenting talks have a * after their name.

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