Comparing compressive sensing and downsampling for COVID-19 diagnosis from cough and speech audio signals

Introduction Since the onset of the COVID-19 pandemic, extensive research has focused on developing non-invasive diagnostic approaches of respiratory syndrome using biomedical signals, particularly cough and speech audio. Time-frequency representations combined with Machine Learning models have shown potential in identifying acoustic biomarkers associated with respiratory conditions. Although many existing approaches demonstrate high performance, their use may be limited in resource-constrained environments due to processing or implementation demands. Methods In this study, we propose an end-to-end approach for COVID-19 inference based on compressed time-domain audio signals. The method combines temporal signal compression strategies - Downsampling (DS) and Compressive Sensing (CS) - with a Convolutional Neural Network (CNN) trained directly on the waveforms. This design eliminates the need for handcrafted features or spectrograms, aiming to reduce computational complexity while preserving classification performance. Results To evaluate the proposed structure, we used data from two open-access datasets, one for coughing and one for speech. Experimental results, assessed using accuracy and F1-score metrics, indicate that CS outperformed DS in most scenarios, particularly under high compression rates (e.g., 200 Hz and 100 Hz). Discussion These findings support the use of compressed audio-based classification in real-world embedded and mobile health systems, where computational efficiency is essential.