Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network

https://doi.org/10.22146/ijccs.75697

Dzikri Rahadian Fudholi(1*), Muhammad Auzan(2), Novia Arum Sari(3)

(1) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(3) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


 Cough is one of the most common symptoms of diseases, especially respiratory diseases. Quick cough detection can be the key to the current pandemic of COVID-19. Good cough recognition is the one that uses non-intrusive tools such as a mobile phone microphone that does not disable human activities like stick sensors. To do sound-only detection, Deep Learning current best method Convolutional Neural Network (CNN) is used. However, CNN needs image input while sound input differs (one dimension rather than two). An extra process is needed, converting sound data to image data using a spectrogram. When building a spectrogram, there is a question about the best size. This research will compare the spectrogram's size, called Spectrogram Window, by the performance. The result is that windows with 4 seconds have the highest F1-score performance at 92.9%. Therefore, a window of around 4 seconds will perform better for sound recognition problems.


Keywords


Spectrogram Window; Convolutional Neural Network; Cough Sound; Deep Learning

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DOI: https://doi.org/10.22146/ijccs.75697

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