Full metadata
Title
Comparative Evaluation of Generative Machine Learning Models for Jazz Improvisation using Numerical Metrics
Description
Standardization is sorely lacking in the field of musical machine learning. This thesis project endeavors to contribute to this standardization by training three machine learning models on the same dataset and comparing them using the same metrics. The music-specific metrics utilized provide more relevant information for diagnosing the shortcomings of each model.
Date Created
2021-12
Contributors
- Hilliker, Jacob (Author)
- Li, Baoxin (Thesis director)
- Libman, Jeffrey (Committee member)
- Barrett, The Honors College (Contributor)
- Computer Science and Engineering Program (Contributor)
Topical Subject
Extent
29 pages
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Series
Academic Year 2021-2022
Handle
https://hdl.handle.net/2286/R.2.N.161046
System Created
- 2021-11-05 01:20:09
System Modified
- 2022-01-28 02:30:05
- 2 years 9 months ago
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