Differentiation of Dry and Wet Cough Sounds using A Deep Learning Model and Data Augmentation
Bibliographic record
Abstract
Automated classification of cough sounds has increased in importance due to factors such as the worldwide COVID-19 pandemic.To train such classification models requires a large dataset of cough sounds; however, it remains challenging to find sufficient expert-labelled training data.This thesis explores a novel form of audio data augmentation, where training cough sounds are corrupted with varying levels of reverberation and Gaussian noise.The combination of noise and reverberation is more effective than both traditional image-based augmentation techniques and either noise or reverberation alone, leading to near-human accuracy on a wet vs. dry cough classification task using a ResNet18 model across two cough datasets.Alignment between the training and testing environments is examined using the Speech-Commands audio dataset.While models trained with the closest reverb and noise level to the test environment gave the best results, the proposed audio augmentation technique produces models with robust performance across test environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| 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".