Diffusion Models for Audio Restoration (2402.09821v3)

Abstract: With the development of audio playback devices and fast data transmission, the demand for high sound quality is rising for both entertainment and communications. In this quest for better sound quality, challenges emerge from distortions and interferences originating at the recording side or caused by an imperfect transmission pipeline. To address this problem, audio restoration methods aim to recover clean sound signals from the corrupted input data. We present here audio restoration algorithms based on diffusion models, with a focus on speech enhancement and music restoration tasks. Traditional approaches, often grounded in handcrafted rules and statistical heuristics, have shaped our understanding of audio signals. In the past decades, there has been a notable shift towards data-driven methods that exploit the modeling capabilities of DNNs. Deep generative models, and among them diffusion models, have emerged as powerful techniques for learning complex data distributions. However, relying solely on DNN-based learning approaches carries the risk of reducing interpretability, particularly when employing end-to-end models. Nonetheless, data-driven approaches allow more flexibility in comparison to statistical model-based frameworks, whose performance depends on distributional and statistical assumptions that can be difficult to guarantee. Here, we aim to show that diffusion models can combine the best of both worlds and offer the opportunity to design audio restoration algorithms with a good degree of interpretability and a remarkable performance in terms of sound quality. We explain the diffusion formalism and its application to the conditional generation of clean audio signals. We believe that diffusion models open an exciting field of research with the potential to spawn new audio restoration algorithms that are natural-sounding and remain robust in difficult acoustic situations.

• The paper presents diffusion models that iteratively denoise corrupted audio signals to restore high-quality speech and music.
• It bridges traditional statistical methods and deep neural networks, offering improved interpretability over conventional end-to-end models.
• The approach achieves significant enhancements in sound clarity and restoration efficiency, with promising applications in modern audio technologies.

The paper "Diffusion Models for Audio Restoration" addresses the increasing demand for high sound quality in both entertainment and communications due to advancements in audio playback devices and rapid data transmission. This paper explores the challenges presented by distortions and interferences that occur during recording or through imperfect transmission pipelines. The central objective is to develop audio restoration algorithms leveraging diffusion models, particularly focusing on speech enhancement and music restoration.

Historically, audio restoration has relied heavily on handcrafted rules and statistical heuristics, which have significantly shaped our understanding of audio signal processing. However, in recent decades, there has been a shift towards data-driven methods that utilize the powerful modeling capabilities of deep neural networks (DNNs). Among these, deep generative models have emerged as particularly powerful for learning complex data distributions. Despite their strengths, DNN-based approaches face challenges related to interpretability, especially when utilizing end-to-end models, which obscure the underlying mechanisms of the learning process.

Diffusion models, as introduced in this paper, present a promising avenue that bridges the gap between traditional statistical models and contemporary data-driven techniques. By doing so, they aim to retain a degree of interpretability while delivering high performance in audio restoration tasks. The authors argue that diffusion models can effectively combine interpretability with the flexibility of DNNs, which is often compromised in purely statistical model-based frameworks due to rigid distributional and statistical assumptions.

In practical terms, diffusion models for audio restoration work by simulating the process of noise addition and removal. The model is trained to progressively denoise a corrupted audio signal, leading to the recovery of the original, clean signal. This approach leverages the iterative nature of diffusion processes to refine and improve sound quality, which is particularly advantageous in handling the intricate details involved in speech and music signals.

In summary, the paper proposes that diffusion models offer a unique solution to audio restoration tasks by balancing the interpretability of traditional methods with the adaptive strengths of deep learning. This hybrid approach shows potential for significant improvements in sound quality, addressing the complexities of modern audio restoration needs.