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

TL;DR

This paper compares two automatic cough segmentation methods, hysteresis comparator and RMS threshold, demonstrating their effectiveness over manual segmentation for preparing data in voice-based disease diagnosis.

Contribution

It evaluates and compares the performance of hysteresis comparator and RMS threshold methods for automatic cough segmentation, providing insights into their accuracy and reliability.

Findings

Automatic methods achieved 73% and 70% precision, outperforming manual segmentation at 49%.

Listening tests showed fair to moderate agreement with automatic segmentation.

Automatic segmentation methods are comparable to manual segmentation in accuracy.

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.