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Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 3 (2022) · pp. 261–270

DOI
10.22146/ijccs.75697
ISSN
2460-7258, 1978-1520
Published
2022-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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

Collaboration network of the authors

13 authors · 21 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Udayana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Paulus Josianlie PUniversitas Gadjah Mada2
M Ridho FuadinUniversitas Gadjah Mada2
Stanislaus Jiwandana PinasthikaUniversitas Gadjah Mada1
Raden Bagus Muhammad AdryanPutra Adhy WijayaAffiliation unknown1
Delfia Nur Anrianti PutriUniversitas Gadjah Mada1
Afiahayati AfiahayatiUniversitas Gadjah Mada1
I Nyoman Prayana TrisnaUdayana University1
Rabbani Nur KumoroUniversitas Gadjah Mada1
Audrey Shafira FattimaUniversitas Gadjah Mada1
William Hilmy SusatyoUniversitas Gadjah Mada1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Spectrogram Window Comparison: Cough Sound Recognition using Convolutional Neural Network</dc:title>
	<dc:creator>Fudholi, Dzikri Rahadian</dc:creator>
	<dc:creator>Auzan, Muhammad</dc:creator>
	<dc:creator>Sari, Novia Arum</dc:creator>
	<dc:subject xml:lang="en-US">Computer</dc:subject>
	<dc:subject xml:lang="en-US">Spectrogram Window; Convolutional Neural Network; Cough Sound; Deep Learning</dc:subject>
	<dc:description xml:lang="en-US"> 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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2022-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/75697</dc:identifier>
	<dc:identifier>10.22146/ijccs.75697</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 3 (2022): July; 261-270</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/75697/34375</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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