Development of automatic cough monitoring, measurement and service system

在这一年中，我们建立了一个高性能的咳嗽检测和监测系统，并利用在FMU收集的数据成功地对其进行了评估。数据集中有11例患者的录音，咳嗽事件时间分布高度不规则。我们的方法是将输入数据分割成10秒长的片段，并分别处理每个片段。我们的模型是基于被称为HuBERT的微调大音频模型，该模型可以高精度地识别咳嗽帧。此外，我们训练了一个特殊的网络来估计每个识别的咳嗽帧在沙发事件中是第一帧，第二帧等的概率。通过这种方式，我们可以区分单独的咳嗽事件，即使它们在一长串咳嗽帧中一个接一个地出现。

During this year we built a high performance cough detection and monitoring system and successfully evaluated it using the data collected at FMU. There are 11 patients audio recordings in the dataset with highly irregular cough events time distribution. Out approach is to segment the input data into 10sec long segments and process each segment separately. Out model is based on fine tuned large audio model called HuBERT which can identify cough frames with high accuracy. In addition, we trained a special network which estimates the probability of each identified cough frame being the first, second, etc, in the couch event. This way, we can distinguish separate cough events even when they come right one after another within a long sequence of cough frames.