SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion
CoRR(2024)
摘要
In this study, we present SingVisio, an interactive visual analysis system
that aims to explain the diffusion model used in singing voice conversion.
SingVisio provides a visual display of the generation process in diffusion
models, showcasing the step-by-step denoising of the noisy spectrum and its
transformation into a clean spectrum that captures the desired singer's timbre.
The system also facilitates side-by-side comparisons of different conditions,
such as source content, melody, and target timbre, highlighting the impact of
these conditions on the diffusion generation process and resulting conversions.
Through comprehensive evaluations, SingVisio demonstrates its effectiveness in
terms of system design, functionality, explainability, and user-friendliness.
It offers users of various backgrounds valuable learning experiences and
insights into the diffusion model for singing voice conversion.
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