Exploring auditory acoustic features for the diagnosis of the Covid-19

Autor:	Kamble, Madhu R., Patino, Jose, Zuluaga, Maria A., Todisco, Massimiliano
Rok vydání:	2022
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted in ICASSP 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2201.09110 Zobrazit plný text záznamu View this record from Arxiv