  Audio denoising, especially in the context of bird sounds, remains a
challenging task due to persistent residual noise. Traditional and deep
learning methods often struggle with artificial or low-frequency noise. In this
work, we propose ViTVS, a novel approach that leverages the power of the vision
transformer (ViT) architecture. ViTVS adeptly combines segmentation techniques
to disentangle clean audio from complex signal mixtures. Our key contributions
encompass the development of ViTVS, introducing comprehensive, long-range, and
multi-scale representations. These contributions directly tackle the
limitations inherent in conventional approaches. Extensive experiments
demonstrate that ViTVS outperforms state-of-the-art methods, positioning it as
a benchmark solution for real-world bird sound denoising applications. Source
code is available at: https://github.com/aiai-4/ViVTS.
