论文标题
birdsoundsdenoising：深层视觉听觉鸟类的声音
BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds
论文作者
论文摘要
数十年来，使用传统和深度学习的方法探索了音频denoising。但是，这些方法仍然仅限于手动添加人造噪声或较低的DeNo Audio质量。为了克服这些挑战，我们收集了一个大规模的天然噪声鸟类声音数据集。我们是第一个将音频降解问题转移到图像分割问题中并提出深层视听denoising（DVAD）模型的人。总共有了14,120张音频图像，我们开发了一个音频成像器工具，并建议使用一些弹性概括策略来标记这些图像。广泛的实验结果表明，所提出的模型实现了最先进的性能。我们还表明，我们的方法很容易被推广到语音denoising，音频分离，音频增强和噪声估计。
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.
