BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds (2022-10-18T00:00:00.000000Z)

TL;DR

The first to transfer the audio denoising problem into an image segmentation problem and propose a deep visual audio Denoising (DVAD) model, which achieves state-of-the-art performance and can be easily generalized to speech denoisation, audio separation, audio enhancement, and noise estimation.

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.

Authors

Youshan Zhang

2 papers

Jialu Li

1 papers

TL;DR

Abstract

Authors

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