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.
翻译:目前,科罗纳病毒的爆发迅速升级,成为一个严重的全球问题,世界卫生组织现已宣布其为国际关注的公共卫生紧急事件。传染病没有国界,因此,控制疾病爆发时,时间绝对必要。在威胁扩散之前,尽早发现威胁非常重要。在DiCOVA第一次成功挑战之后,组织者释放了第二个DiCOVA挑战,目的是通过呼吸、咳嗽和语音音频样本对COVID-19进行诊断。这项工作展示了使用呼吸、咳嗽和语音录音进行COVID-19自动检测系统的细节。我们开发了不同的前端听觉功能,同时开发了双向长程短距离记忆(Bi-LSTM)作为分类器。结果很有希望,并展示了Breathing、Cough和语音音轨的听力功能之间的高度互补行为,测试集显示86.60%的AUC。