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arXiv · 2210.10196

BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

Youshan Zhang·Jialu Li
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Abstract

Audio denoising has been explored for decades using both traditional and deep learning-based methods. However, these methods are still limited to either manually added artificial noise or lower denoised audio quality. To overcome these challenges, we collect a large-scale natural noise bird sound dataset. We are the first to transfer the audio denoising problem into an image segmentation problem and propose a deep visual audio denoising (DVAD) model. With a total of 14,120 audio images, we develop an audio ImageMask tool and propose to use a few-shot generalization strategy to label these images. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance. We also show that our method can be easily generalized to speech denoising, audio separation, audio enhancement, and noise estimation.

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Youshan Zhang, Jialu Li. 2022-10-18. BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds. https://arxiv.org/abs/2210.10196

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