Comparison of Machine Learning Algorithms on Classification of Covid-19 Cough Sounds Using MFCC Extraction
Mohammad Reza Faisal
reza.faisal@ulm.ac.idLambung Mangkurat University (Indonesia)
Muhammad Thoriq Hidayat
Lambung Mangkurat University (Indonesia)
Dwi Kartini
Lambung Mangkurat University (Indonesia)
Fatma Indriani
Lambung Mangkurat University (Indonesia)
Irwan Budiman
Lambung Mangkurat University (Indonesia)
Triando Hamonangan Saragih
Lambung Mangkurat University (Indonesia)
Abstract
Early detection for COVID-19 has now been widely developed. One of the methods used is cough audio detection. This research aims to classify cough audio. Audio feature extraction is performed using MFCC to obtain numerical features. Feature classification is done using SVM, Random Forest, and Naive Bayes methods. Evaluation is done to find the best classification method. The evaluation results in this study show that SVM Kernel RBF produces the best evaluation value with an AUC value of 0.657715.
Keywords:
audio cough, SVM, Random Forest, Naive BayesReferences
S. Hassantabar, M. Ahmadi, A. Sharifi, Diagnosis and detection of infected tissue of COVID-19 patients based on lung X-ray image using convolutional neural network approaches, Chaos Solitons Fractals 140 (2020) 110-170, https://doi.org/10.1016/j.chaos.2020.110170.
DOI: https://doi.org/10.1016/j.chaos.2020.110170
Google Scholar
V. Bansal, G. Pahwa, N. Kannan, Cough Classification for COVID-19 based on audio mfcc features using Con-volutional Neural Networks, in 2020 IEEE International Conference on Computing, Power and Communication Technologies (GUCON) IEEE (2020) 604–608, https://doi.org/10.1109/GUCON48875.2020.9231094.
DOI: https://doi.org/10.1109/GUCON48875.2020.9231094
Google Scholar
R. Matin, D. Valles, A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions, in 2020 Intermountain Engineering, Technology and Computing (IETC) IEEE (2020) 1–6, https://doi.org/10.1109/IETC47856.2020.9249147.
DOI: https://doi.org/10.1109/IETC47856.2020.9249147
Google Scholar
G. Karaca, Y. Kutlu, Turkish voice commands based chess game using gammatone cepstral coefficients, arXiv preprint arXiv:2101.08441 (2021) https://doi.org/10.48550/arXiv.2101.08441.
Google Scholar
N. Chauhan, T. Isshiki, D. Li, Speaker Recognition Using LPC, MFCC, ZCR Features with ANN and SVM Classi-fier for Large Input Database, in 2019 IEEE 4th Interna-tional Conference on Computer and Communication Sys-tems (ICCCS), IEEE (2019) 130–133, https://doi.org/10.1109/CCOMS.2019.8821751.
DOI: https://doi.org/10.1109/CCOMS.2019.8821751
Google Scholar
S. R. Chaudhary, S. N. Kakarwal, J. V. Bagade, Feature selection and classification of indian musical string in-struments using SVM, Indian Journal of Computer Sci-ence and Engineering 12(4) (2021) 859–867, https://doi.org/10.21817/indjcse/2021/v12i4/211204142.
DOI: https://doi.org/10.21817/indjcse/2021/v12i4/211204142
Google Scholar
L. O. Iheme, Ş. Ozan, Multiclass digital audio segmenta-tion with MFCC features using naive Bayes and SVM classifiers, in 2019 Innovations in Intelligent Systems and Applications Conference (ASYU) (2019) 1–5, https://doi.org/10.1109/ASYU48272.2019.8946441.
DOI: https://doi.org/10.1109/ASYU48272.2019.8946441
Google Scholar
N. Ndou, R. Ajoodha, A. Jadhav, Music genre classifica-tion: A review of deep-learning and traditional machine-learning approaches, in 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRON-ICS) (2021) 1–6, https://doi.org/10.1109/IEMTRONICS52119.2021.9422487.
DOI: https://doi.org/10.1109/IEMTRONICS52119.2021.9422487
Google Scholar
M. Pahar, M. Klopper, R. Warren, T. Niesler, COVID-19 cough classification using machine learning and global smartphone recordings, Comput Biol. Med. 135 (2021) https://doi.org/10.1016/j.compbiomed.2021.104572.
DOI: https://doi.org/10.1016/j.compbiomed.2021.104572
Google Scholar
I. Södergren, M. P. Nodeh, P. C. Chhipa, K. Nikolaidou, G. Kovács, Detecting COVID-19 from Audio Recording of Coughs Using Random Forests and Support Vector Machines, in Interspeech 2021, ISCA (2021) 916–920, https://doi.org/10.21437/Interspeech.2021-2191.
DOI: https://doi.org/10.21437/Interspeech.2021-2191
Google Scholar
M. M. Gauy, M. Finger, Audio MFCC-gram Transform-ers for respiratory insufficiency detection in COVID-19, arXiv preprint arXiv:2210.14085 (2022) https://doi.org/10.48550/arXiv.2210.14085.
DOI: https://doi.org/10.5753/stil.2021.17793
Google Scholar
M. B. Alsabek, I. Shahin, A. Hassan, Studying the Simi-larity of COVID-19 Sounds based on Correlation Analy-sis of MFCC, in 2020 International Conference on Com-munications, Computing, Cybersecurity, and Informatics (CCCI), IEEE (2020) 1–5, https://doi.org/10.1109/CCCI49893.2020.9256700.
DOI: https://doi.org/10.1109/CCCI49893.2020.9256700
Google Scholar
A. S. Elkorany, M. Marey, K. M. Almustafa, Z. F. El-sharkawy, Breast Cancer Diagnosis Using Support Vector Machines Optimized by Whale Optimization and Dragonfly Algorithms, IEEE Access 10 (2022) 69688–69699, https://doi.org/10.1109/ACCESS.2022.3186021.
DOI: https://doi.org/10.1109/ACCESS.2022.3186021
Google Scholar
A. Razaque, M. Ben Haj Frej, M. Almi’ani, M. Alotaibi, B. Alotaibi, Improved support vector machine enabled radial basis function and linear variants for remote sensing image classification, Sensors 21(13) (2021) 4431, doi: https://doi.org/10.3390/s21134431.
DOI: https://doi.org/10.3390/s21134431
Google Scholar
E. Kubera, A. Wieczorkowska, A. Kuranc, and T. Słowik, Discovering Speed Changes of Vehicles from Audio Data, Sensors 19(14) (2019) 3067, https://doi.org/10.3390/s19143067.
DOI: https://doi.org/10.3390/s19143067
Google Scholar
K. Palanisamy, D. Singhania, and A. Yao, Rethinking CNN models for audio classification, arXiv preprint arXiv:2007.11154, (2020) https://doi.org/10.48550/arXiv.2007.11154.
Google Scholar
Y. Zeng, H. Mao, D. Peng, Z. Yi, Spectrogram based multi-task audio classification, Multimed Tools Appl, 78(3) (2019) 3705–3722, https://doi.org/10.1007/s11042-017-5539-3.
DOI: https://doi.org/10.1007/s11042-017-5539-3
Google Scholar
N. Fazakis, V. G. Kanas, C. K. Aridas, S. Karlos, S. Kotsiantis, Combination of active learning and semi-supervised learning under a self-training scheme, Entropy 21(10) (2019) 988, https://doi.org/10.3390/e21100988.
DOI: https://doi.org/10.3390/e21100988
Google Scholar
D. Berrar, Cross-Validation, in Encyclopedia of Bioin-formatics and Computational Biology, Tokyo: Elsevier (2019) 542–545, https://doi.org/10.1016/B978-0-12-809633-8.20349-X.
DOI: https://doi.org/10.1016/B978-0-12-809633-8.20349-X
Google Scholar
V. Maeda-Gutiérrez et al., Comparison of convolutional neural network architectures for classification of tomato plant diseases, Applied Sciences 10(4) (2020) 1245 https://doi.org/10.3390/app10041245.
DOI: https://doi.org/10.3390/app10041245
Google Scholar
J. R. Maria Navin, R. Pankaja, Performance analysis of text classification algorithms using confusion matrix, In-ternational Journal of Engineering and Technical Research (IJETR) 6(4) (2016) 75–78.
Google Scholar
Authors
Muhammad Thoriq HidayatLambung Mangkurat University Indonesia
Authors
Dwi KartiniLambung Mangkurat University Indonesia
Authors
Fatma IndrianiLambung Mangkurat University Indonesia
Authors
Irwan BudimanLambung Mangkurat University Indonesia
Authors
Triando Hamonangan SaragihLambung Mangkurat University Indonesia
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