Adaptive Differential Denoising for Respiratory Sounds Classification
Journal:
arXiv
Published Date:
Jun 3, 2025
Abstract
Automated respiratory sound classification faces practical challenges from
background noise and insufficient denoising in existing systems.
We propose Adaptive Differential Denoising network, that integrates noise
suppression and pathological feature preservation via three innovations:
1) Adaptive Frequency Filter with learnable spectral masks and soft shrink to
eliminate noise while retaining diagnostic high-frequency components;
2) A Differential Denoise Layer using differential attention to reduce
noise-induced variations through augmented sample comparisons;
3) A bias denoising loss jointly optimizing classification and robustness
without clean labels.
Experiments on the ICBHI2017 dataset show that our method achieves 65.53\% of
the Score, which is improved by 1.99\% over the previous sota method.
The code is available in https://github.com/deegy666/ADD-RSC
