The usage of smartphone-collected respiratory sound, trained with deep
learning models, for detecting and classifying COVID-19 becomes popular
recently. It removes the need for in-person testing procedures especially for
rural regions where related medical supplies, experienced workers, and
equipment are limited. However, existing sound-based diagnostic approaches are
trained in a fully supervised manner, which requires large scale well-labelled
data. It is critical to discover new methods to leverage unlabelled respiratory
data, which can be obtained more easily. In this paper, we propose a novel
self-supervised learning enabled framework for COVID-19 cough classification. A
contrastive pre-training phase is introduced to train a Transformer-based
feature encoder with unlabelled data. Specifically, we design a random masking
mechanism to learn robust representations of respiratory sounds. The
pre-trained feature encoder is then fine-tuned in the downstream phase to
perform cough classification. In addition, different ensembles with varied
random masking rates are also explored in the downstream phase. Through
extensive evaluations, we demonstrate that the proposed contrastive
pre-training, the random masking mechanism, and the ensemble architecture
contribute to improving cough classification performance.

本文提出了一种基于自监督学习的框架，通过对未标记的数据进行对比性预训练，从而在 COVID-19 疾病状态下对咳嗽进行分类。