Recognising Covid-19 from Coughing Using Ensembles of SVMs and LSTMs with Handcrafted and Deep Audio Features

As the Covid-19 pandemic continues, digital health solutions can provide valuable insights and assist in diagnosis and prevention. Since the disease affects the respiratory system, it is hypothesised that sound formation is changed, and thus, an infection can be automatically recognised through audio analysis. We present an ensemble learning approach used in our entry to Track 1 of the DiCOVA 2021 Challenge, which aims at binary classification of Covid-19 infection on a crowd-sourced dataset of 1 040 cough sounds. Our system is based on a combination of handcrafted features for paralinguistics with deep feature extraction from spectrograms using pre-trained CNNs. We extract features both at segment level and with a sliding window approach, and process them with SVMs and LSTMs, respectively. We then perform least-squares weighted late fusion of our classifiers. Our system surpasses the challenge baseline, with a ROC-AUC on the test set of 78.18%.