Millions of people have died worldwide from COVID-19. In addition to its high
death toll, COVID-19 has led to unbearable suffering for individuals and a huge
global burden to the healthcare sector. Therefore, researchers have been trying
to develop tools to detect symptoms of this human-transmissible disease
remotely to control its rapid spread. Coughing is one of the common symptoms
that researchers have been trying to detect objectively from smartphone
microphone-sensing. While most of the approaches to detect and track cough
symptoms rely on machine learning models developed from a large amount of
patient data, this is not possible at the early stage of an outbreak. In this
work, we present an incremental transfer learning approach that leverages the
relationship between healthy peoples' coughs and COVID-19 patients' coughs to
detect COVID-19 coughs with reasonable accuracy using a pre-trained healthy
cough detection model and a relatively small set of patient coughs, reducing
the need for large patient dataset to train the model. This type of model can
be a game changer in detecting the onset of a novel respiratory virus.

利用预训练的健康咳嗽检测模型和相对较小的病人咳嗽数据集，我们提出了一种增量迁移学习方法，通过利用健康人咳嗽和 COVID-19 患者咳嗽之间的关系来合理准确地检测 COVID-19 咳嗽，减少了大规模病人数据集用于训练模型的需求。这种模型对于侦测新型呼吸病毒的发作具有重要意义。