Adaptive Metadata-Guided Supervised Contrastive Learning for Domain Adaptation on Respiratory Sound Classification
Bibliographic record
Abstract
Despite considerable advancements in deep learning, optimizing respiratory sound classification (RSC) models remains challenging. This is partly due to the bias from inconsistent respiratory sound recording processes and imbalanced representation of demographics, which leads to poor performance when a model trained with the dataset is applied to real-world use cases. RSC datasets usually include various metadata attributes describing certain aspects of the data, such as environmental and demographic factors. To address the issues caused by bias, we take advantage of the metadata provided by RSC datasets and explore approaches for metadata-guided domain adaptation. We thoroughly evaluate the effect of various metadata attributes and their combinations on a simple metadata-guided approach, but also introduce a more advanced method that adaptively rescales the suitable metadata combinations to improve domain adaptation during training. The findings indicate a robust reduction in domain dependency and improvement in detection accuracy on both ICBHI and our own dataset. Specifically, the implementation of our proposed methods led to an improved score of 84.97%, which signifies a substantial enhancement of 7.37% compared to the baseline model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".