Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report
This work addresses the problem of developing clinically robust audio-based COVID-19 detection systems for healthcare applications, but it is incremental as it applies existing methods to new data with demographic controls.
This paper tackled COVID-19 detection using audio models by fine-tuning pre-trained architectures like Audio-MAE and PANN on datasets such as Coswara and COUGHVID, with results showing moderate intra-dataset performance (e.g., Audio-MAE achieved 0.82 AUC on Coswara) but severe generalization failure in cross-dataset evaluations (AUC 0.43-0.68).
This technical report investigates the performance of pre-trained audio models on COVID-19 detection tasks using established benchmark datasets. We fine-tuned Audio-MAE and three PANN architectures (CNN6, CNN10, CNN14) on the Coswara and COUGHVID datasets, evaluating both intra-dataset and cross-dataset generalization. We implemented a strict demographic stratification by age and gender to prevent models from exploiting spurious correlations between demographic characteristics and COVID-19 status. Intra-dataset results showed moderate performance, with Audio-MAE achieving the strongest result on Coswara (0.82 AUC, 0.76 F1-score), while all models demonstrated limited performance on Coughvid (AUC 0.58-0.63). Cross-dataset evaluation revealed severe generalization failure across all models (AUC 0.43-0.68), with Audio-MAE showing strong performance degradation (F1-score 0.00-0.08). Our experiments demonstrate that demographic balancing, while reducing apparent model performance, provides more realistic assessment of COVID-19 detection capabilities by eliminating demographic leakage - a confounding factor that inflate performance metrics. Additionally, the limited dataset sizes after balancing (1,219-2,160 samples) proved insufficient for deep learning models that typically require substantially larger training sets. These findings highlight fundamental challenges in developing generalizable audio-based COVID-19 detection systems and underscore the importance of rigorous demographic controls for clinically robust model evaluation.