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Nudge Demo.mp4

ContributorsDoyle, Niklas (Author) / Osburn, Steven (Thesis director) / Miller, Phillip (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

06 - Golden Heart FINAL MIX.wav

Description
This creative project focused on the process of making an album from scratch. From songwriting to recording, this three-year project ended with the release of a ten-song album available on all streaming platforms. The goal of this project was to experience the same general process as professional recording artists and…

This creative project focused on the process of making an album from scratch. From songwriting to recording, this three-year project ended with the release of a ten-song album available on all streaming platforms. The goal of this project was to experience the same general process as professional recording artists and gain a better understanding of the music industry.
ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

02 - Where Your Worth Is From FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

04 - The One FINAL MIX (1).wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

09 - I'm Never Going Back FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

07 - Smell the Roses FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

03 - Dear Future Me FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

05 - Let Them See FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

01 - Grow FINAL MIX.wav

ContributorsStone, Amanda (Author) / Myers, Nathan (Thesis director) / Steiner, Kiernan (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / School of Music, Dance and Theatre (Contributor)
Created2023-05

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…

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.
ContributorsHilliker, Jacob (Author) / Li, Baoxin (Thesis director) / Libman, Jeffrey (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2021-12