BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

TL;DR

This paper introduces a novel deep visual audio denoising model that transforms audio denoising into an image segmentation task, utilizing a large natural noise bird sound dataset to achieve state-of-the-art results and generalize to other audio applications.

Contribution

The paper presents the first application of image segmentation techniques to audio denoising, with a large-scale dataset and a few-shot labeling strategy for improved performance.

Findings

Achieves state-of-the-art denoising performance

Successfully generalizes to speech denoising and audio separation

Introduces a large-scale natural noise bird sound dataset

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.