Impact of face covering models on respiratory sound classification applications
Bibliographic record
Abstract
Respiratory sound evaluation and classification has the potential to provide healthcare professionals with information that would otherwise be unavailable, especially in light of the COVID-19 pandemic. With the adoption of face masks and cough covering best practices, understanding the impact of face coverings on recorded audio measurements is essential. In this paper, system identification has been applied to four face covering states (disposable mask, N95 mask, fabric mask, and elbow covering) leading to four transfer functions that can be applied pre-recorded vocal sounds. As covering a cough with a bent elbow led to the highest level of frequency attenuation, it was used to evaluate three classifiers created using the original uncovered data, the elbow covered modeled data, and a combination of both. Each classifier used YAMNet embeddings to classify between four respiratory sounds. The classifier built using the original uncovered and modeled elbow covered data led to the highest performance when evaluated on either the uncovered or modeled data, with accuracies of 0.72. The application of these models can not only evaluate the robustness of preexisting respiratory classifiers in the presence of face coverings but may also be used as a data augmentation tool for human vocal sounds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".