COVID-19 respiratory sound analysis and classification using audio textures
Bibliographic record
Abstract
Since the COVID-19 outbreak, a major scientific effort has been made by researchers and companies worldwide to develop a digital diagnostic tool to screen this disease through some biomedical signals, such as cough, and speech. Joint time–frequency feature extraction techniques and machine learning (ML)-based models have been widely explored in respiratory diseases such as influenza, pertussis, and COVID-19 to find biomarkers from human respiratory system-generated acoustic sounds. In recent years, a variety of techniques for discriminating textures and computationally efficient local texture descriptors have been introduced, such as local binary patterns and local ternary patterns, among others. In this work, we propose an audio texture analysis of sounds emitted by subjects in suspicion of COVID-19 infection using time–frequency spectrograms. This approach of the feature extraction method has not been widely used for biomedical sounds, particularly for COVID-19 or respiratory diseases. We hypothesize that this textural sound analysis based on local binary patterns and local ternary patterns enables us to obtain a better classification model by discriminating both people with COVID-19 and healthy subjects. Cough, speech, and breath sounds from the INTERSPEECH 2021 ComParE and Cambridge KDD databases have been processed and analyzed to evaluate our proposed feature extraction method with ML techniques in order to distinguish between positive or negative for COVID-19 sounds. The results have been evaluated in terms of an unweighted average recall (UAR). The results show that the proposed method has performed well for cough, speech, and breath sound classification, with a UAR up to 100.00%, 60.67%, and 95.00%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".