AUTHOR=Silva Leticia , Valadão Carlos , Lampier Lucas , Delisle-Rodríguez Denis , Caldeira Eliete , Bastos-Filho Teodiano , Krishnan Sridhar 

TITLE=COVID-19 respiratory sound analysis and classification using audio textures

JOURNAL=Frontiers in Signal Processing

VOLUME=Volume 2 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/signal-processing/articles/10.3389/frsip.2022.986293

DOI=10.3389/frsip.2022.986293

ISSN=2673-8198

ABSTRACT=Since the COVID-19 outbreak, major scientific effort has been done 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 the 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, 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 for 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 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 Accuracy (ACC), and Area Under the Curve of Receiver Operating Characteristic (AUC-ROC). The results show that the proposed method has performed well for cough, speech, and breath sounds classification, with ACC up to 100.00%, 72.10%, and 85.70%, respectively, to infer COVID-19 infection, which serves as an effective tool to perform a preliminary screening of COVID-19.