AUTHOR=Silva Leticia , Floriano Alan , Valadão Carlos , Caldeira Eliete , Krishnan Sridhar , Bastos Filho Teodiano 
  
TITLE=Comparing compressive sensing and downsampling for COVID-19 diagnosis from cough and speech audio signals
  
JOURNAL=Frontiers in Signal Processing
  
VOLUME=Volume 5 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2025.1700044
  
DOI=10.3389/frsip.2025.1700044
  
ISSN=2673-8198
  
ABSTRACT=IntroductionSince 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.MethodsIn 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.ResultsTo 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).DiscussionThese findings support the use of compressed audio-based classification in real-world embedded and mobile health systems, where computational efficiency is essential.