Classification of COVID-19 Cough Sounds using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Support Vector Machine

Muhammad Meftah Mafazy, Mohammad Reza Faisal, Dwi Kartini, Fatma Indriani, Triando Hamonangan Saragih

Abstract


A lot of research has been carried out to detect COVID-19, such as swabs, rapid antigens, and using x-ray images. However, this method has the disadvantage that it requires taking samples through physical contact with the patient. One way to avoid physical contact is to use audio through coughing with the aim of reducing the transmission of COVID-19. Audio feature extraction such as the Mel Frequency Cepstral Coefficient (MFCC) has often been used in audio classification research, such as the classification of musical genres and so on. This study aims to compare more or less the features of audio classification performance through coughing sounds for early detection of COVID-19 using a Support Vector Machine based on the Linear and Radial Basis Function (RBF). The dataset used is the COVID-19 Cough audio dataset, before classifying, the audio data is processed into a spectrogram and then feature extraction is carried out. Classification is divided into 2 schemes, using default parameters, then using the specified configuration parameters. From the research results, the highest AUC is 0.572266 in the linear kernel-based SVM classification. Meanwhile, when using the RBF kernel, the highest AUC is 0.560181.

Keywords


COVID-19 Feature Extraction Classification Audio Cough SVM

Full Text:

Link Download

References


Bansal, V., Pahwa, G., & Kannan, N. (2020). Cough classification for COVID-19 based on audio mfcc features using convolutional neural networks. 2020 IEEE International Conference on Computing, Power and Communication Technologies, GUCON 2020 (pp. 604–608). Institute of Electrical and Electronics Engineers Inc.

Berrar D. (2018) Cross-validation. Encyclopedia of Bioinformatics and Computational Biology, Volume 1, Elsevier, pp. 542–545. https://doi.org/10.1016/B978-0-12-809633-8.20349-X

Chowdhury, N. K., Kabir, M. A., Rahman, M. M., & Islam, S. M. S. (2022). Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Computers in Biology and Medicine, 145. Elsevier Ltd.

Danika, A., Raharjo, J., & Hidayat, B. (2022). Deteksi Suara Gitar Dengan Bahan Jenis Senar Berbeda Melalui Ciri Akustik Dengan Mel-Frequency Cepstral Coefficients (MFCC) Dan Support Vector Machine (SVM) Guitar String Detection Through Acoustic Characteristics Using Mel-Frequency Cepstral Coefficients. e-Proceeding of Engineering, 8, 2936–2942.

Han, J., Brown, C., Chauhan, J., Grammenos, A., Hasthanasombat, A., Spathis, D., Xia, T., et al. (2021). Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 8328–8332).

Huang, B., Zhu, Y., Wang, Z., & Fang, Z. (2021). Imbalanced data classification algorithm based on clustering and SVM. Journal of Circuits, Systems and Computers, 30(02), 2150036. World Scientific.

McFee, B., McVicar, M., Faronbi, D., Roman, I., Gover, M., Balke, S., Seyfarth, S., et al. (2023, March). librosa/librosa: 0.10.0.post2. Zenodo.

Naicker, N., Adeliyi, T., & Wing, J. (2020). Linear support vector machines for prediction of student performance in school-based education. Mathematical Problems in Engineering, 2020, 1–7. Hindawi Limited.

Nugroho, C. A. (2021). Klasifikasi k-nearest neighbor chest X-ray pasien Covid-19 dengan haralick features dan histogram of oriented gradient. MATHunesa: Jurnal Ilmiah Matematika, 9(1), 188–195.

Pahar, M., Klopper, M., Reeve, B., Warren, R., Theron, G., & Niesler, T. (2021). Automatic cough classification for tuberculosis screening in a real-

world environment. Physiological Measurement, 42(10), 105014. IOP Publishing.

Pambudi, R. (2022). Deteksi Penggunaan Masker dengan Algoritma RBF Support Vector Machine. The Journal on Machine Learning and Computational Intelligence (JMLCI), 1(2).

Primartha, R. (2018). Belajar Machine Learning Teori dan Praktik. Bandung: Informatika Bandung, 10, 20–30.

Putri, R. N. (2020). Indonesia dalam Menghadapi Pandemi Covid-19. Jurnal Ilmiah Universitas Batanghari Jambi, 20(2), 705. Universitas Batanghari Jambi.

Ritwik, K. V. S., Kalluri, S. B., & Vijayasenan, D. (2021). COVID-19 Detection from Spectral Features on the DiCOVA Dataset. Interspeech (pp. 936–940).

Södergren, I., Nodeh, M. P., Chhipa, P. C., Nikolaidou, K., & Kovács, G. (2021). Detecting COVID-19 from audio recording of coughs using Random Forests and Support Vector Machines. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH (Vol. 6, pp. 4256–4260). International Speech Communication Association.

Solehudin, R. (2018). Implementasi Metode MFCC (Mel Frequency Cepstral Coefficient) Dan Naive Bayesian Untuk Klasifikasi Nada Dasar Gitar. Universitas Komputer Indonesia.

Tena, A., Clarià, F., & Solsona, F. (2022). Automated detection of COVID-19 cough. Biomedical Signal Processing and Control, 71, 103175.

Yanti, B., Ismida, F. D., & Sarah, K. E. S. (2020). Perbedaan uji diagnostik antigen, antibodi, RT-PCR dan tes cepat molekuler pada Coronavirus Disease 2019. Jurnal Kedokteran Syiah Kuala, 20(3).

Zeng, Y., Mao, H., Peng, D., & Yi, Z. (2019). Spectrogram based multi-task audio classification. Multimedia Tools and Applications, 78, 3705–3722. Springer.




DOI: http://dx.doi.org/10.35671/telematika.v16i2.2569

Refbacks

  • There are currently no refbacks.


 


Indexed by:

   

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .