Audio texture analysis of COVID-19 cough, breath, and speech sounds

https://doi.org/10.1016/j.bspc.2022.103703Get rights and content

Highlights

  • Our proposed model can classify 5 types of cough sounds with an accuracy rate of 71.7%, 5 types of breath sounds with an accuracy rate of 72.2%, and 79.7% of speech sounds. The system offers the highest accuracy rate of 98.9% while performing binary classification on COVID-19 and non-COVID-19 cough sounds.

  • To our knowledge, this is the first time that the audio textural analysis on COVID-19 cough and breath sounds was done on following five different classes: COVID-19 positive with cough, COVID-19 positive without cough, healthy person with cough, healthy person without cough, and an asthmatic cough.

  • The proposed work is one of first works in which audio texture is explored to screen COVID-19 sounds i.e. cough, breath, and speech.

Abstract

The coronavirus disease (COVID-19) first appeared at the end of December 2019 and is still spreading in most countries. To diagnose COVID-19 using reverse transcription - Polymerase chain reaction (RT-PCR), one has to go to a dedicated center, which requires significant cost and human resources. Hence, there is a requirement for a remote monitoring tool that can perform the preliminary screening of COVID-19. In this paper, we propose that a detailed audio texture analysis of COVID-19 sounds may help in performing the initial screening of COVID-19. The texture analysis is done on three different signal modalities of COVID-19, i.e. cough, breath, and speech signals. In this work, we have used 1141 samples of cough signals, 392 samples of breath signals, and 893 samples of speech signals. To analyze the audio textural behavior of COVID-19 sounds, the local binary patterns LBP) and Haralick’s features were extracted from the spectrogram of the signals. The textural analysis on cough and breath sounds was done on the following 5 classes for the first time: COVID-19 positive with cough, COVID-19 positive without cough, healthy person with cough, healthy person without cough, and an asthmatic cough. For speech sounds there were only two classes: COVID-19 positive, and COVID-19 negative. During experiments, 71.7% of the cough samples and 72.2% of breath samples were classified into 5 classes. Also, 79.7% of speech samples are classified into 2 classes. The highest accuracy rate of 98.9% was obtained when binary classification between COVID-19 cough and non-COVID-19 cough was done.

Keywords

COVID-19
Cough
Speech
Breath
Audio texture
Spectrogram
Local binary pattern
Haralick features

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