# Fine-tuning Pre-trained Audio Models for COVID-19 Detection: A Technical Report

arXiv: https://arxiv.org/abs/2511.14939 · HTML: https://mlsanity.com/paper/2511.14939/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 14 of 14)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [PANNs: Large-Scale Pretrained Audio Neural Networks for Audio Pattern Recognition](https://mlsanity.com/paper/1912.10211/) (2019)
- [Masked Autoencoders that Listen](https://mlsanity.com/paper/2207.06405/) (2022)
- [The COUGHVID crowdsourcing dataset: A corpus for the study of large-scale cough analysis algorithms](https://mlsanity.com/paper/2009.11644/) (2020)
- [Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis](https://mlsanity.com/paper/2005.10548/) (2020)
- [Coswara: A respiratory sounds and symptoms dataset for remote screening of SARS-CoV-2 infection](https://mlsanity.com/paper/2305.12741/) (2023)
- [Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applications](https://mlsanity.com/paper/1804.09028/) (2018)
- [Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers](https://mlsanity.com/paper/2212.08570/) (2022)
- [A Semi-Supervised Algorithm for Improving the Consistency of Crowdsourced Datasets: The COVID-19 Case Study on Respiratory Disorder Classification](https://mlsanity.com/paper/2209.04360/) (2022)
- [COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers](https://mlsanity.com/paper/2207.09529/) (2022)
- [A large-scale and PCR-referenced vocal audio dataset for COVID-19](https://mlsanity.com/paper/2212.07738/) (2022)
- [Robust COVID-19 Detection from Cough Sounds using Deep Neural Decision Tree and Forest: A Comprehensive Cross-Datasets Evaluation](https://mlsanity.com/paper/2501.01117/) (2025)
- [Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data Augmentation](https://mlsanity.com/paper/2210.05843/) (2022)
- [Contrasting Deep Learning Models for Direct Respiratory Insufficiency Detection Versus Blood Oxygen Saturation Estimation](https://mlsanity.com/paper/2407.20989/) (2024)

## Abstract (arXiv)

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.