  Cough is a common symptom of respiratory and lung diseases. Cough detection
is important to prevent, assess and control epidemic, such as COVID-19. This
paper proposes a model to detect cough events from cough audio signals. The
models are trained by the dataset combined ESC-50 dataset with self-recorded
cough recordings. The test dataset contains inpatient cough recordings
collected from inpatients of the respiratory disease department in Ruijin
Hospital. We totally build 15 cough detection models based on different feature
numbers selected by Random Frog, Uninformative Variable Elimination (UVE), and
Variable influence on projection (VIP) algorithms respectively. The optimal
model is based on 20 features selected from Mel Frequency Cepstral Coefficients
(MFCC) features by UVE algorithm and classified with Support Vector Machine
(SVM) linear two-class classifier. The best cough detection model realizes the
accuracy, recall, precision and F1-score with 94.9%, 97.1%, 93.1% and 0.95
respectively. Its excellent performance with fewer dimensionality of the
feature vector shows the potential of being applied to mobile devices, such as
smartphones, thus making cough detection remote and non-contact.
