A Dynamic-Static Dual Input Deep Neural Network Algorithm for Diagnosing COVID-19 by Cough
Tien Tzu Hsueh Pao/Acta Electronica Sinica
; 51(1):202-212, 2023.
Artículo
en Chino
| Scopus | ID: covidwho-20245323
ABSTRACT
The COVID-19 (corona virus disease 2019) has caused serious impacts worldwide. Many scholars have done a lot of research on the prevention and control of the epidemic. The diagnosis of COVID-19 by cough is non-contact, low-cost, and easy-access, however, such research is still relatively scarce in China. Mel frequency cepstral coefficients (MFCC) feature can only represent the static sound feature, while the first-order differential MFCC feature can also reflect the dynamic feature of sound. In order to better prevent and treat COVID-19, the paper proposes a dynamic-static dual input deep neural network algorithm for diagnosing COVID-19 by cough. Based on Coswara dataset, cough audio is clipped, MFCC and first-order differential MFCC features are extracted, and a dynamic and static feature dual-input neural network model is trained. The model adopts a statistic pooling layer so that different length of MFCC features can be input. The experiment results show the proposed algorithm can significantly improve the recognition accuracy, recall rate, specificity, and F1-score compared with the existing models. © 2023 Chinese Institute of Electronics. All rights reserved.
audio technology; CNN; cough; COVID-19; deep learning; Mel frequency cepstral coefficients; Deep neural networks; Diagnosis; Disease control; Viruses; Audio technologies; Corona virus disease 2019; Dynamic features; First-order differentials; Mel frequency cepstral co-efficient; Mel-frequency cepstral coefficients; Neural networks algorithms; Virus disease; Neural network models
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Estudios diagnósticos
/
Estudio pronóstico
Idioma:
Chino
Revista:
Acta Electronica Sinica
Año:
2023
Tipo del documento:
Artículo
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