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