Complex Image Generation SwinTransformer Network for Audio Denoising
Youshan Zhang Jialu Li
Achieving high-performance audio denoising is still a challenging task in
real-world applications. Existing time-frequency methods often ignore the
quality of generated frequency domain images. This paper converts the audio
denoising problem into an image generation task. We first develop a complex
image generation SwinTransformer network to capture more information from the
complex Fourier domain. We then impose structure similarity and detailed loss
functions to generate high-quality images and develop an SDR loss to minimize
the difference between denoised and clean audios. Extensive experiments on two
benchmark datasets demonstrate that our proposed model is better than
state-of-the-art methods.