In this paper, an algorithm designed to detect characteristic cough events in audio recordings is presented, significantly reducing the time required for manual counting. Using time-frequency representations and independent subspace analysis (ISA), sound events that exhibit characteristics of coughs are automatically detected, producing a summary of the events detected. Using a dataset created from publicly available audio recordings, this algorithm has been tested on a variety of synthesized audio scenarios representative of those likely to be encountered by subjects undergoing an ambulatory cough recording, achieving a true positive rate of 76% with an average of 2.85 false positives per minute.

翻译：本文介绍了一种旨在检测录音中典型咳嗽事件的算法,大大缩短了人工计时所需的时间。使用时间-频率表和独立的子空间分析(ISA),自动检测出显示咳嗽特征的音响事件,对所检测到的事件进行总结。使用从公开的录音中创建的数据集,该算法在各种综合音频情景上进行了测试,这些情景代表了进行流动咳嗽记录的人可能遇到的情况,实现了76%的真实正率,平均每分钟2.85个假正数。