Papers › Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound...
Patch-Mix Contrastive Learning with Audio Spectrogram Transformer on Respiratory Sound Classification
Sangmin Bae, June-Woo Kim, Won-Yang Cho, Hyerim Baek, Soyoun Son, Byungjo Lee, Changwan Ha, Kyongpil Tae, Sungnyun Kim, Se-Young Yun
Respiratory sound contains crucial information for the early diagnosis of fatal lung diseases. Since the COVID-19 pandemic, there has been a growing interest in contact-free medical care based on electronic stethoscopes. To this end, cutting-edge deep learning models have been developed to diagnose lung diseases; however, it is still challenging due to the scarcity of medical data. In this study, we demonstrate that the pretrained model on large-scale visual and audio datasets can be generalized to the respiratory sound classification task. In addition, we introduce a straightforward Patch-Mix augmentation, which randomly mixes patches between different samples, with Audio Spectrogram Transformer (AST). We further propose a novel and effective Patch-Mix Contrastive Learning to distinguish the mixed representations in the latent space. Our method achieves state-of-the-art performance on the ICBHI dataset, outperforming the prior leading score by an improvement of 4.08%.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Audio Classification | ICBHI Respiratory Sound Database | AST (Patch-Mix CL) | ICBHI Score | 62.37 | #9 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | AST (Patch-Mix CL) | Sensitivity | 43.07 | #9 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | AST (Patch-Mix CL) | Specificity | 81.66 | #9 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | AST (fine-tuning) | ICBHI Score | 59.55 | #13 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | AST (fine-tuning) | Sensitivity | 41.97 | #24 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | AST (fine-tuning) | Specificity | 77.14 | #24 of 25 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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