Complex Image-Generative Diffusion Transformer for Audio Denoising

TL;DR

This paper introduces a novel complex image-generative diffusion transformer for audio denoising, leveraging the Fourier domain and diffusion models to improve performance over existing methods.

Contribution

It presents the first application of diffusion transformers to audio denoising, integrating Fourier domain analysis with scalable attention mechanisms.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Expands receptive field using attention diffusion

Demonstrates scalability of the transformer model

Abstract

The audio denoising technique has captured widespread attention in the deep neural network field. Recently, the audio denoising problem has been converted into an image generation task, and deep learning-based approaches have been applied to tackle this problem. However, its performance is still limited, leaving room for further improvement. In order to enhance audio denoising performance, this paper introduces a complex image-generative diffusion transformer that captures more information from the complex Fourier domain. We explore a novel diffusion transformer by integrating the transformer with a diffusion model. Our proposed model demonstrates the scalability of the transformer and expands the receptive field of sparse attention using attention diffusion. Our work is among the first to utilize diffusion transformers to deal with the image generation task for audio denoising. Extensive experiments on two benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods.