Papers › Lung Sound Classification Using Co-tuning and Stochastic Normalization
Lung Sound Classification Using Co-tuning and Stochastic Normalization
Truc Nguyen, Franz Pernkopf
In this paper, we use pre-trained ResNet models as backbone architectures for classification of adventitious lung sounds and respiratory diseases. The knowledge of the pre-trained model is transferred by using vanilla fine-tuning, co-tuning, stochastic normalization and the combination of the co-tuning and stochastic normalization techniques. Furthermore, data augmentation in both time domain and time-frequency domain is used to account for the class imbalance of the ICBHI and our multi-channel lung sound dataset. Additionally, we apply spectrum correction to consider the variations of the recording device properties on the ICBHI dataset. Empirically, our proposed systems mostly outperform all state-of-the-art lung sound classification systems for the adventitious lung sounds and respiratory diseases of both datasets.
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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 | ResNet-50 | ICBHI Score | 58.29 | #14 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | ResNet-50 | Sensitivity | 37.24 | #14 of 25 | Archive leaderboard | report |
| Audio Classification | ICBHI Respiratory Sound Database | ResNet-50 | Specificity | 79.34 | #14 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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