Cough Classification with Deep Derived Features using Audio Spectrogram Transformer
Bibliographic record
Abstract
Cough diagnosis is important for the elderly population, since cough is a key symptom of many respiratory illnesses and conditions. This paper introduces a Transformer-based feature learning approach for the analysis of cough recordings. A Transformer network leveraging feature learning on a big data set is investigated from a feature engineering perspective, in order to find dedicated classification models that can improve overall performance. The latter was achieved through adopting AutoML post-processing techniques on different data sets, driven by the feature engineering process based on both feature selection and feature generation via nonlinear methods. It was found that this approach led to substantial improvements (in the order of 17% from 0.818 to 0.956 of accuracy) on practically all metrics of classification performance, with respect t o t hose obtained with standalone Transformers. Moreover, AutoML models using reduced number of features, either selected or generated, resulted in higher quality models. In particular, a model working only with 1.2 % of the features (nonlinearly generated from the 768 produced by the Transformer), outperformed the model using all of them. These results highlight that big data-derived machine learning models, when post-processed, can play an important role in adapting to small-data scenarios.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".