  Coughing is a typical symptom of COVID-19. To detect and localize coughing
sounds remotely, a convolutional neural network (CNN) based deep learning model
was developed in this work and integrated with a sound camera for the
visualization of the cough sounds. The cough detection model is a binary
classifier of which the input is a two second acoustic feature and the output
is one of two inferences (Cough or Others). Data augmentation was performed on
the collected audio files to alleviate class imbalance and reflect various
background noises in practical environments. For effective featuring of the
cough sound, conventional features such as spectrograms, mel-scaled
spectrograms, and mel-frequency cepstral coefficients (MFCC) were reinforced by
utilizing their velocity (V) and acceleration (A) maps in this work. VGGNet,
GoogLeNet, and ResNet were simplified to binary classifiers, and were named
V-net, G-net, and R-net, respectively. To find the best combination of features
and networks, training was performed for a total of 39 cases and the
performance was confirmed using the test F1 score. Finally, a test F1 score of
91.9% (test accuracy of 97.2%) was achieved from G-net with the MFCC-V-A
feature (named Spectroflow), an acoustic feature effective for use in cough
detection. The trained cough detection model was integrated with a sound camera
(i.e., one that visualizes sound sources using a beamforming microphone array).
In a pilot test, the cough detection camera detected coughing sounds with an F1
score of 90.0% (accuracy of 96.0%), and the cough location in the camera image
was tracked in real time.
