Research on diagnosing diseases based on voice signals is rapidly increasing, including cough-related diseases. When training the cough sound signals into deep learning models, it is necessary to have a standard input by segmenting several cough signals into individual cough signals. Segmenting coughs could also be used to monitor trends of cough-related disease by counting the number of coughs. Previous research has been developed to segment cough signals from non-cough signals. This research evaluates the segmentation methods of several cough signals from a single audio file into several audio files containing a single file. We evaluate three different methods, including manual segmentation as a baseline and two automatic segmentation methods: hysteresis comparator and root mean square (RMS) methods. The results by two automatic segmentation methods obtained precisions of 73% (hysteresis) and 70% (RMS) compared to 49% by manual segmentation. The agreements of listening tests to count the number of correct single-cough segmentations show fair and moderate correlations for automatic segmentation methods and are comparable with manual segmentation.

Original languageEnglish
Pages (from-to)5-12
Number of pages8
JournalInternational Journal of Information Technology (Singapore)
Issue number1
Publication statusPublished - Jan 2024


  • Cough segmentation
  • Hysteresis comparator
  • RMS threshold
  • Signal processing
  • Voice analyses


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