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arXiv · 2210.02057

Comparing Hysteresis Comparator and RMS Threshold Methods for Automatic Single Cough Segmentations

Bagus Tris Atmaja·Zanjabila·Suyanto·Akira Sasou
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Abstract

Research on diagnosing diseases based on voice signals currently are 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. 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 single-cough signals. We evaluate three different methods employing manual segmentation as a baseline and automatic segmentation. The results by two automatic segmentation methods obtained precisions of 73% and 70% 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.

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Bagus Tris Atmaja, Zanjabila, Suyanto, Akira Sasou. 2022-10-05. Comparing Hysteresis Comparator and RMS Threshold Methods for Automatic Single Cough Segmentations. https://doi.org/10.1007/s41870-023-01626-8

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