  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.
