Automated Covid-19 Respiratory Symptoms Analysis from Speech and Cough

Pleva, Matúš; Martens, Elien; Juhár, Jozef

doi:10.1109/sami54271.2022.9780815

Cited by 3 publications

(2 citation statements)

References 14 publications

(17 reference statements)

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“…Since the COVID-19 outbreak, several research studies have been conducted to infer infection by COVID-19 (Brown et al, 2020;Schuller et al, 2021;Zhou et al, 2021;Pahar et al, 2022;Pleva et al, 2022;Sharma et al, 2022;Villa-Parra et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…In the same direction, various research studies employing respiratory sounds were conducted for COVID-19 screening (Brown et al, 2020;Casanova et al, 2021;Schuller et al, 2021;Verde et al, 2021;Pahar et al, 2022;Pleva et al, 2022;Sharma et al, 2022;Villa-Parra et al, 2022). Cough and breathing sounds from COVID-19, asthmatic, and healthy individuals were utilized by Brown et al (2020).…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 respiratory sound analysis and classification using audio textures

Silva

Valadão²,

Lampier³

et al. 2022

Front. Signal Process.

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Since the COVID-19 outbreak, a major scientific effort has been made by researchers and companies worldwide to develop a digital diagnostic tool to screen this disease through some biomedical signals, such as cough, and speech. Joint time–frequency feature extraction techniques and machine learning (ML)-based models have been widely explored in respiratory diseases such as influenza, pertussis, and COVID-19 to find biomarkers from human respiratory system-generated acoustic sounds. In recent years, a variety of techniques for discriminating textures and computationally efficient local texture descriptors have been introduced, such as local binary patterns and local ternary patterns, among others. In this work, we propose an audio texture analysis of sounds emitted by subjects in suspicion of COVID-19 infection using time–frequency spectrograms. This approach of the feature extraction method has not been widely used for biomedical sounds, particularly for COVID-19 or respiratory diseases. We hypothesize that this textural sound analysis based on local binary patterns and local ternary patterns enables us to obtain a better classification model by discriminating both people with COVID-19 and healthy subjects. Cough, speech, and breath sounds from the INTERSPEECH 2021 ComParE and Cambridge KDD databases have been processed and analyzed to evaluate our proposed feature extraction method with ML techniques in order to distinguish between positive or negative for COVID-19 sounds. The results have been evaluated in terms of an unweighted average recall (UAR). The results show that the proposed method has performed well for cough, speech, and breath sound classification, with a UAR up to 100.00%, 60.67%, and 95.00%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.

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