Cough Sound Analysis using Vocal Tract Models
Bibliographic record
Abstract
Coughing is one of the most common symptoms of respiratory disorders, and variations in the cough sound relate to factors such as the type and quantity of secretions, physiological differences in the airway, and the force of the expulsion of the air. Automated analysis of spontaneous cough sounds in a smart home can provide a non-contact, non-invasive method to identify changes in health status, and passively monitor the progress of conditions such as chronic lung disease, to support independence and aging in place. In this work we propose analyzing and characterizing coughs using vocal tract models originally developed for speech coding. These models can relate cough sounds to physiological features, helping provide the interpretable and explainable predictions that are necessary for trust and confidence in healthcare applications. We show that linear prediction can effectively capture the time and frequency dynamics of different cough phases. We also demonstrate the interpretability of the model parameters by developing features to distinguish wet and dry cough sounds. The features achieve perfect linear separation of coughs in a small physician-labelled dataset and provide insight into the sound characteristics that contribute to those descriptors. The compact representation of cough sounds provided by the parametric model approach motivates further investigation for embedded cough analysis applications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".