Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion
(2402.10009)Abstract
Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models. The first, which we coin ZEro-shot Text-based Audio (ZETA) editing, is adopted from the image domain. The second, named ZEro-shot UnSupervized (ZEUS) editing, is a novel approach for discovering semantically meaningful editing directions without supervision. When applied to music signals, this method exposes a range of musically interesting modifications, from controlling the participation of specific instruments to improvisations on the melody. Samples and code can be found in https://hilamanor.github.io/AudioEditing/ .
Overview
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Introduces a new approach for zero-shot audio editing using denoising diffusion probabilistic models (DDPMs) for both unsupervised and text-based editing, aimed at sophisticated audio manipulations without model retraining.
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Explores the constrained landscape of audio editing, traditionally relying on task-specific models and test-time optimization, and proposes a versatile paradigm through zero-shot editing via pre-trained diffusion models.
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Details methodologies including a DDPM inversion technique for extracting latent noise vectors for editing, a text-based method using textual prompts for guidance, and an unsupervised method identifying editing directions in the model's noise space.
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Demonstrates superior performance of the proposed methods over existing models in generating semantically meaningful and high-quality audio edits, suggesting broad applicability and potential for future creative and flexible applications.
Zero-Shot Unsupervised and Text-Based Audio Editing via DDPM Inversion
Introduction
Recent progress in generative models, particularly diffusion models, has shown promising results in image synthesis and editing. However, audio signal editing, especially in a zero-shot and unsupervised manner, remains a fundamentally challenging area due to its intricate temporal and harmonic complexities. The paper introduces a novel approach to zero-shot audio editing by leveraging denoising diffusion probabilistic models (DDPMs) for both unsupervised and text-based editing, marking a significant stride toward sophisticated audio manipulations without the need for exhaustive model retraining or fine-tuning.
Related Work
The landscape of audio editing, traditionally dominated by task-specific trained models and test-time optimization techniques, lacks the flexibility and ease seen in recent advancements within the image domain. Despite these methods enabling fine-grained audio manipulations, their reliance on extensive datasets for training and computational intensity at inference time poses substantial limitations. The emergence of zero-shot editing using pre-trained diffusion models presents a more versatile paradigm, albeit underexplored within the audio context. This backdrop sets the stage for the proposed methodologies in this paper, framing them as an extension and adaptation of image domain techniques to tackle the unique challenges of audio signal editing.
Methodology
DDPM Inversion
The foundation of the proposed methods lies in an "edit-friendly" DDPM inversion technique, adapted from prior work in the image domain. This inversion process extracts latent noise vectors from a given audio signal, which are then utilized to steer the DDPM generation process towards desired edits. Two distinct approaches are proposed: a text-based editing method that relies on textual prompts to guide the editing process, and an innovative unsupervised editing method that identifies semantically meaningful editing directions within the noise space of the diffusion model.
Text-Based Editing
The text-based editing approach employs text prompts to describe the desired outcome and, optionally, the original signal. This allows for a broad spectrum of audio manipulations, from stylistic changes to specific instrumental alterations, while maintaining high fidelity to the original audio's perceptual and semantic qualities. This method leverages the classifier-free guidance mechanism to balance adherence to the textual description and the original audio structure.
Unsupervised Editing
In contrast, the unsupervised editing method does not rely on textual descriptions but discovers editing directions in an unsupervised manner directly from the diffusion model's noise space. This is achieved by perturbing the denoiser's output along the principal components of the posterior distribution's covariance, enabling a diverse range of edits that are semantically meaningful yet difficult to specify textually.
Experimental Results
The proposed methods were evaluated against state-of-the-art models like MusicGen and other zero-shot editing techniques such as SDEdit across various metrics. Results demonstrate superior performance in generating semantically meaningful and perceptually high-quality edits, with the unsupervised method unveiling novel and musically intriguing modifications. The effectiveness of the methods extends across diverse audio signals, showcasing their versatility and broad applicability.
Implications and Future Directions
The introduction of DDPM inversion for zero-shot, unsupervised audio editing enriches the toolkit for audio manipulation, enabling more creative and flexible applications. By circumventing the need for dataset-specific model training or extensive optimization, these methods can significantly streamline the audio editing workflow. Future research could explore the integration of these techniques with other types of media, such as video or interactive applications, and the development of more intuitive interfaces for specifying edits. The potential for further refining the unsupervised method to extract even more nuanced semantic directions also presents an exciting avenue for future work.
Conclusion
This paper establishes a foundational approach for zero-shot audio editing using DDPM inversion, offering both text-based and unsupervised methodologies. These techniques not only push the boundaries of what's possible in audio editing but also pave the way for more advanced and user-friendly editing tools capable of accommodating a wider range of creative expressions.
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