CoBrS: Cough Breath Segmentation for the Reduction of Class-Confounding Characteristics in Dataset Curation
Alice E. Ashby, Khuong An Nguyen · 2024
Cough segmentation using Machine Learning is known to be sensitive to the effects of class-confounding char-acteristics in the training data, significantly skewing predictions with the introduction of bias. Mechanisms by which bias may permeate a dataset include small sample sizes and noise in the samples. In this paper, we propose a novel audio segmentation algorithm as a means to solve these issues through automatic isolation and extraction of biological audio events. Our algorithm, CoBrS, is based on heuristics derived from physiological assumptions and is designed to accurately isolate all cough types, including the complex peal cough, and provides segmentation support for breaths, a previously undocumented modality in segmentation literature. CoBrS was validated on three public cough datasets with varying segmentation complexity (Coswara, COUGHVID, Virufy) against two state-of-the-art algorithms (COUGHVID and Virufy), achieving mean signal quality increases of 169.3%, 274.2%, and 39.8%, and sample size increases of 250% and 280 % respectively. Our findings were also manually verified by two human raters who reported a 94% peal cough segmentation rate and that 88 % of coughs in the moderate noise test subset are of high quality. Our algorithm is capable of effectively isolating cough and breath events of all types from samples with low to moderate noise, whilst improving signal quality and retaining high-frequency information that is often lost in the process.