Covid-19 Detection From Cough Recordings Using Spatial Representations and Convolutional Neural Networks

Targeting reliable Covid-19 detection, we report the results of a comparative analysis focused on using three distinct types of spatial representations of cough recordings applied as inputs to Convolutional Neural Networks (CNN’s). The actual algorithms used to transform the original time series into images are based on the classical Mel Frequency Cepstral Coefficients (MFCC) spectrogram, a modified version of the Continuous Wavelet Transform (the S-Transform), and the Gramian Angular Fields (GAF). Extensive experiments have been conducted on the COUGHVID and COVID-19 Sounds cough datasets, using various CNN’s architectures. The MFCC based approach yielded best results, comparing favourably to existing reported performances.