Wednesday, 4/6/2022
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University
Shawnee State University
Major
Digital Simulation and Gaming Engineering Technology
Presentation Types
Poster Group Presentation
Keywords:
Machine learning, voice recognition, voice generation, computer-generated music
Abstract
By representing audio information as a many-dimensional vector, it is possible to derive the characteristic eigenvectors of this audio through data manipulation techniques. From these “eigenwaves”, sounds can be identified or created. The identification of audio waves using eigenwaves has the potential to be useful in many practical applications ranging from human voice recognition to the creation of authentic-sounding computer-generated voices. Some limitations of the proposed method also will be described.
Human Subjects
no
Faculty Mentor Name
R. Duane Skaggs
Faculty Mentor Title
Associate Professor of Digital Simulation and Gaming
Faculty Mentor Academic Department
Engineering Technologies
Recommended Citation
Wickerham, Elijah; Cobb, Tyler; Daniels, Kyle; and Fadley, Tristen, "Eigenwaves" (2022). Celebration of Scholarship. 9.
https://digitalcommons.shawnee.edu/cos/2022/day3/9
Eigenwaves
By representing audio information as a many-dimensional vector, it is possible to derive the characteristic eigenvectors of this audio through data manipulation techniques. From these “eigenwaves”, sounds can be identified or created. The identification of audio waves using eigenwaves has the potential to be useful in many practical applications ranging from human voice recognition to the creation of authentic-sounding computer-generated voices. Some limitations of the proposed method also will be described.