  Objective: Lung auscultation is a valuable tool in diagnosing and monitoring
various respiratory diseases. However, lung sounds (LS) are significantly
affected by numerous sources of contamination, especially when recorded in
real-world clinical settings. Conventional denoising models prove impractical
for LS denoising, primarily owing to spectral overlap complexities arising from
diverse noise sources. To address this issue, we propose a specialized
deep-learning model (Uformer) for lung sound denoising. Methods: The proposed
Uformer model is constituted of three modules: a Convolutional Neural Network
(CNN) encoder module, dedicated to extracting latent features; a Transformer
encoder module, employed to further enhance the encoding of unique LS features
and effectively capture intricate long-range dependencies; and a CNN decoder
module, employed to generate the denoised signals. An ablation study was
performed in order to find the most optimal architecture. Results: The
performance of the proposed Uformer model was evaluated on lung sounds induced
with different types of synthetic and real-world noises. Lung sound signals of
-12 dB to 15 dB signal-to-noise ratio (SNR) were considered in testing
experiments. The proposed model showed an average SNR improvement of 16.51 dB
when evaluated with -12 dB LS signals. Our end-to-end model, with an average
SNR improvement of 19.31 dB, outperforms the existing model when evaluated with
ambient noise and fewer parameters. Conclusion: Based on the qualitative and
quantitative findings in this study, it can be stated that Uformer is robust
and generalized to be used in assisting the monitoring of respiratory
conditions.
