COVID-19 Detection using Audio Spectral Features and Machine Learning
Michael Esposito, Sunil Rao, Vivek Narayanaswamy, Andreas Spanias · 2021 55th Asilomar Conference on Signals, Systems, and Computers · 2021
In this research and education REU project, we use audio waveform signatures of coughing to determine whether COVID-19 can be diagnosed. More specifically, we determine coughing audio spectral features and use neural network architectures to develop diagnostics for COVID-19. The non-invasive rapid and remote testing benefits of this approach relative to existing nose swab, saliva, and blood testing make this method attractive for deployment on smart phones. Challenges include distorted or low-quality audio samples, availability of reliable labeled data, confusability with other respiratory diseases, and lack of baseline (healthy) audio recordings for comparison. We have studied, compared, tuned and implemented in Python an array of convolutional neural network architectures. Results using a unique parallel machine learning architecture with a fusion unit are presented.