mlsanityData to 7 Oct 2026

BirdSoundsDenoising: Deep Visual Audio Denoising for Bird Sounds

arXiv 2210.10196 ↗First posted 18 Oct 2022cs.SD cs.CV eess.AScited by 7

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Abstract (arXiv)

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.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 16 of its 42 references that are in the map. First posted on arXiv 18 Oct 2022.

Builds on

16 of 42 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,210
  3. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  4. 2021Segmenter: Transformer for Semantic Segmentationcited by 855
  5. 2020U2-Net: Going Deeper with Nested U-Structure for Salient Object Detectioncited by 410
  6. 2021Mixed Transformer U-Net For Medical Image Segmentationcited by 95
  7. 2021TSTNN: Two-stage Transformer based Neural Network for Speech Enhancement in the Time Domaincited by 36
  8. 2018Speech Denoising with Deep Feature Lossescited by 26
  9. 2016A Fully Convolutional Neural Network for Speech Enhancementcited by 23
  10. 2021Speech Denoising Without Clean Training Data: A Noise2Noise Approachcited by 17
  11. 2022A Two-Stage U-Net for High-Fidelity Denoising of Historical Recordingscited by 17
  12. 2020Listening to Sounds of Silence for Speech Denoisingcited by 9
4 more
  1. 2021Speech enhancement with weakly labelled data from AudioSetcited by 9
  2. 2019Improving Deep Speech Denoising by Noisy2Noisy Signal Mappingcited by 6
  3. 2020A Review of Multi-Objective Deep Learning Speech Denoising Methodscited by 6
  4. 2021A Modulation-Domain Loss for Neural-Network-based Real-time Speech Enhancementcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 2; 2025: 1; 2026: 1.

7 papers.

  1. 2023Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Reviewcited by 5
  2. 2023Complex Image Generation SwinTransformer Network for Audio Denoisingcited by 3
  3. 2023DPATD: Dual-Phase Audio Transformer for Denoisingcited by 2
  4. 2024Diffusion Gaussian Mixture Audio Denoisecited by 1
  5. 2024Complex Image-Generative Diffusion Transformer for Audio Denoisingcited by 1
  6. 2025Multi-Representation Attention Framework for Underwater Bioacoustic Denoising and Recognitioncited by 0
  7. 2026Zero-Shot and Supervised Bird Image Segmentation Using Foundation Models: A Dual-Pipeline Approach with Grounding DINO~1.5, YOLOv11, and SAM~2.1cited by 0