论文标题
探索用于诊断Covid-19的听觉声学特征
Exploring auditory acoustic features for the diagnosis of the Covid-19
论文作者
论文摘要
目前的冠状病毒爆发已迅速升级，成为一个严重的全球问题，现已被世界卫生组织宣布为国际关注的公共卫生紧急情况。传染病不知道边界，因此在控制爆发时，时机绝对是必不可少的。在传播之前，尽早发现威胁是如此重要。在首次成功的Dicova挑战之后，组织者发布了第二次Dicova挑战，目的是通过使用呼吸，咳嗽和语音音频样本来诊断Covid-19。这项工作介绍了使用呼吸，咳嗽和语音记录的自动系统的详细信息。我们开发了不同的前端听觉声学特征以及双向长期记忆（BI-LSTM）作为分类器。结果是有希望的，并且已经证明了呼吸，咳嗽和语音轨道中听觉声学特征之间的互补行为高，在测试集中，AUC为86.60％。
The current outbreak of a coronavirus, has quickly escalated to become a serious global problem that has now been declared a Public Health Emergency of International Concern by the World Health Organization. Infectious diseases know no borders, so when it comes to controlling outbreaks, timing is absolutely essential. It is so important to detect threats as early as possible, before they spread. After a first successful DiCOVA challenge, the organisers released second DiCOVA challenge with the aim of diagnosing COVID-19 through the use of breath, cough and speech audio samples. This work presents the details of the automatic system for COVID-19 detection using breath, cough and speech recordings. We developed different front-end auditory acoustic features along with a bidirectional Long Short-Term Memory (bi-LSTM) as classifier. The results are promising and have demonstrated the high complementary behaviour among the auditory acoustic features in the Breathing, Cough and Speech tracks giving an AUC of 86.60% on the test set.
