Abstract
The automatic identification of cough segments in audio through the determination of start and end points is pivotal to building scalable screening tools in health technologies for pulmonary related diseases. We propose the application of two current pre-trained architectures to the task of cough activity detection. A dataset of recordings containing cough from patients symptomatic for tuberculosis (TB) who self-present at community-level care centres in South Africa and Uganda is employed. When automatic start and end points are determined using XLS-R, an average precision of 0.96 and an area under the receiver-operating characteristic of 0.99 are achieved for the test set. We show that best average precision is achieved by utilising only the first three layers of the network, which has the dual benefits of reduced computational and memory requirements, pivotal for smartphone-based applications. This XLS-R configuration is shown to outperform an audio spectrogram transformer (AST) as well as a logistic regression baseline by 9% and 27% absolute in test set average precision respectively. Furthermore, a downstream TB classification model trained using the coughs automatically isolated by XLS-R comfortably outperforms a model trained on the coughs isolated by AST, and is only narrowly outperformed by a classifier trained on the ground truth coughs. We conclude that the application of large pre-trained transformer models is an effective approach to identifying cough end-points and that the integration of such a model into a screening tool is feasible.
Abstract (translated)
通过确定咳嗽段的起始和结束点来自动识别音频中的咳嗽片段，对于构建用于肺部相关疾病的可扩展筛查工具至关重要。我们提议将两个当前预训练的架构应用于咳嗽活动检测任务中。该研究使用了一组记录数据集，其中包含南非和乌干达社区级护理中心自诊为结核病（TB）患者发出的咳嗽声。 当使用XLS-R自动确定起始和结束点时，在测试集中实现了平均精度0.96和接收者操作特性曲线下面积（AUC-ROC）为0.99的成绩。我们展示了通过仅利用网络的前三个层可以获得最佳的平均精度，这不仅减少了计算资源和内存需求，对基于智能手机的应用来说尤其重要。 该XLS-R配置在测试集平均精度上分别比音频光谱图变换器（AST）和逻辑回归基线高出9%和27%，显示了其优越性。此外，使用XLS-R自动隔离的咳嗽训练出的下游TB分类模型明显优于基于AST隔离的咳嗽训练出的模型，并且仅略微逊于基于真实数据标签训练的分类器。 综上所述，我们认为应用大规模预训练转换模型来识别咳嗽结束点是有效的方法，并且将此类模型集成到筛查工具中是可行的。
URL
https://arxiv.org/abs/2603.11241
