Cough Detection Using Selected Informative Features from Audio Signals
14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
; 2021.
Article
in English
| Scopus | ID: covidwho-1672582
ABSTRACT
Cough is a common symptom of respiratory and lung diseases. Cough detection is important to prevent, assess and control epidemic, such as COVID-19. This paper proposes a model to detect cough events from cough audio signals. The models are trained by the dataset combined ESC-50 dataset with self-recorded cough recordings. The test dataset contains inpatient cough recordings collected from inpatients of the respiratory disease department in Ruijin Hospital. We totally build 15 cough detection models based on different feature numbers selected by Random Frog, Uninformative Variable Elimination (UVE), and Variable influence on projection (VIP) algorithms respectively. The optimal model is based on 20 features selected from Mel Frequency Cepstral Coefficients (MFCC) features by UVE algorithm and classified with Support Vector Machine (SVM) linear two-class classifier. The best cough detection model realizes the accuracy, recall, precision and F1-score with 94.9%, 97.1%, 93.1% and 0.95 respectively. Its excellent performance with fewer dimensionality of the feature vector shows the potential of being applied to mobile devices, such as smartphones, thus making cough detection remote and non-contact. © 2021 IEEE.
Disease control; Statistical tests; Support vector machines; Audio signal; Classifieds; Detection models; Mel frequency cepstral co-efficient; Mel-frequency cepstral coefficients; Model-based OPC; Optimal model; Projection algorithms; Support vectors machine; Uninformative variable eliminations; Feature extraction
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
14th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2021
Year:
2021
Document Type:
Article
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