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SingVisio: Visual Analytics of Diffusion Model for Singing Voice Conversion (2402.12660v2)

Published 20 Feb 2024 in cs.SD, cs.HC, and eess.AS

Abstract: 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 comparative and 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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Summary

  • The paper introduces SingVisio, a system that offers interactive visual analytics to elucidate the diffusion process in singing voice conversion.
  • It maps hidden diffusion features onto a two-dimensional plane and integrates Mel spectrograms for clear audio quality visualization.
  • Extensive evaluations confirm SingVisio’s effectiveness, with objective accuracy around 85.88% and high usability scores from expert studies.

Visual Analytics for Understanding Singing Voice Conversion with Diffusion Models: Introducing SingVisio

Overview of SingVisio

In the domain of deep generative models for data generation tasks, the advent of diffusion-based generative models has marked a significant stride, particularly in the field of singing voice conversion (SVC). To elucidate the complex workings of these models, SingVisio is introduced as an interactive visual analysis system. Its primary aim is to render the diffusion process intelligible through visual displays of the generation process, including the denoising of noisy spectrums and transformation into clean spectrums, and to facilitate side-by-side comparisons under various conditions impacting the diffusion generation process and outcomes.

Key Contributions

SingVisio stands out with several noteworthy contributions to the field of visual analytics for diffusion-based SVC:

  • It pioneers as a system supporting exploration, visualization, and comparison of the diffusion model within SVC, offering a versatile platform for a detailed examination of various aspects of the diffusion process.
  • The introduction of a novel interactive approach for understanding diffusion-based SVC through data-driven, condition-driven, and evaluation-driven exploration modes amplifies its utility.
  • Through comprehensive evaluations, including case and expert studies, SingVisio's effectiveness in enhancing system design, functionalities, explainability, and user-friendliness is confirmed.

Technical Details and System Design

SingVisio intricately maps hidden features of the diffusion model onto a two-dimensional plane, facilitating visual comparisons to uncover patterns. It integrates Mel spectrograms to depict audio quality through various stages of the diffusion process. A novel comparative visualization strategy enables intuitive investigations of different conditions, embedding source and target audio references directly into the interface.

The system comprises several views, including the Metric View for objective evaluation results, the Projection View for tracking data patterns, the Step View for visualizing the Mel spectrogram at any given diffusion step, the Comparison View for comparing voice conversion results, and the Control Panel for selecting various comparison modes and conditions.

Evaluation and Insights

Extensive evaluations underscore SingVisio's efficacy. Objective assessments spotlight an average accuracy of 85.88% across tasks, highlighting its robustness in enabling users to understand the diffusion process and its implications for SVC. Subjective assessments yield an average score of 4.44 out of 5, evidencing its positive reception among users regarding its explainability, functionality, and usability.

Through case and expert studies, SingVisio is lauded for its interactive design, facilitating deep insight into the diffusion model's mechanics and SVC. It particularly aids in distinguishing the impact of various conditions on SVC outcomes, offering unparalleled understanding and interpretability of the complex diffusion process.

Future Directions

SingVisio sets the stage for future developments in AI by pioneering visual analytics of diffusion models in SVC. Its innovative approach not only demystifies the intricate workings of these advanced models but also opens avenues for further research into making complex AI models interpretable through visual analytics. This could extend to other domains beyond SVC, harnessing SingVisio's core principles to elucidate complex generative models across varied AI applications.

Closing Thoughts

As SingVisio elucidates the complexities of diffusion-based singing voice conversion models through interactive visual analytics, it heralds a new chapter in the understanding and application of these advanced generative models. By providing a comprehensive tool that enhances learning, explanation, and analysis of SVC, SingVisio significantly contributes to advancing both theoretical and practical knowledge in the field, paving the way for innovative future research directions in visual analytics for AI.

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