Cough Classification with Deep Derived Features using Audio Spectrogram Transformer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it