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https://hdl.handle.net/2142/129612
Description
Title
Text recaptioning for audio diffusion models
Author(s)
Matthews, Evan Michael
Issue Date
2025-05-05
Director of Research (if dissertation) or Advisor (if thesis)
Smaragdis, Paris
Department of Study
Siebel School Comp & Data Sci
Discipline
Computer Science
Degree Granting Institution
University of Illinois Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
diffusion
audio
generative models
generative audio
sound
text recaptioning
recaptioning
text prompts
Abstract
Textual Inversion (TI) is one of the most recent discoveries in generative audio models that allows for prompt recaptioning of a pretrained model’s concept understanding. In this thesis, we explore a rudimentary method and TI for audio sample outputs, specifically focusing on a custom-trained TI model that embeds new concepts into pretrained image models. The TI recaptioned samples are compared with baseline samples, and we find a significant difference in variation between baseline samples. We believe that this comparison will provide a strong foundation and inspiration to future works related to prompt recaptions and analysis on generative audio models.
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