Abstract：
OBJECTIVE: Cough audio signal classification is a potentially useful tool in screening for respiratory disorders, such as COVID-19. Since it is dangerous to collect data from patients with contagious diseases, many research teams have turned to crowdsourcing to quickly gather cough sound data. The COUGHVID dataset enlisted expert physicians to annotate and diagnose the underlying diseases present in a limited number of recordings. However, this approach suffers from potential cough mislabeling, as well as disagreement between experts.
METHODS: In this work, we use a semi-supervised learning (SSL) approach - based on audio signal processing tools and interpretable machine learning models - to improve the labeling consistency of the COUGHVID dataset for 1) COVID-19 versus healthy cough sound classification 2) distinguishing wet from dry coughs, and 3) assessing cough severity. First, we leverage SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and label sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classifiers.
RESULTS: The consistency of the re-labeled COVID-19 and healthy data is demonstrated in that it exhibits a high degree of inter-class feature separability: 3x higher than that of the user-labeled data. Similarly, the SSL method increases this separability by 11.3x for cough type and 5.1x for severity classifications. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs in the 1-1.5 kHz range (p=1.2×10-64), which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a COVID-19 classifier, achieving an AUC of 0.797.
CONCLUSIONS: We propose a SSL expert knowledge aggregation technique for the field of cough sound classification for the first time, and demonstrate how it can be used to combine the medical knowledge of multiple experts in an explainable fashion, thus providing abundant, consistent data for cough classification tasks.
METHODS: In this work, we use a semi-supervised learning (SSL) approach - based on audio signal processing tools and interpretable machine learning models - to improve the labeling consistency of the COUGHVID dataset for 1) COVID-19 versus healthy cough sound classification 2) distinguishing wet from dry coughs, and 3) assessing cough severity. First, we leverage SSL expert knowledge aggregation techniques to overcome the labeling inconsistencies and label sparsity in the dataset. Next, our SSL approach is used to identify a subsample of re-labeled COUGHVID audio samples that can be used to train or augment future cough classifiers.
RESULTS: The consistency of the re-labeled COVID-19 and healthy data is demonstrated in that it exhibits a high degree of inter-class feature separability: 3x higher than that of the user-labeled data. Similarly, the SSL method increases this separability by 11.3x for cough type and 5.1x for severity classifications. Furthermore, the spectral differences in the user-labeled audio segments are amplified in the re-labeled data, resulting in significantly different power spectral densities between healthy and COVID-19 coughs in the 1-1.5 kHz range (p=1.2×10-64), which demonstrates both the increased consistency of the new dataset and its explainability from an acoustic perspective. Finally, we demonstrate how the re-labeled dataset can be used to train a COVID-19 classifier, achieving an AUC of 0.797.
CONCLUSIONS: We propose a SSL expert knowledge aggregation technique for the field of cough sound classification for the first time, and demonstrate how it can be used to combine the medical knowledge of multiple experts in an explainable fashion, thus providing abundant, consistent data for cough classification tasks.
摘要：
目的：咳嗽音频信号分类是筛查呼吸系统疾病的潜在有用工具，比如COVID-19。因为从传染病患者那里收集数据是危险的，许多研究团队已经转向众包，以快速收集咳嗽声音数据。COUGHVID数据集邀请专家医师来注释和诊断有限数量的记录中存在的潜在疾病。然而,这种方法有潜在的咳嗽错误标签，以及专家之间的分歧。
方法：在这项工作中，我们使用半监督学习(SSL)方法-基于音频信号处理工具和可解释的机器学习模型-提高COUGHVID数据集的标记一致性，用于1)COVID-19与健康咳嗽声音分类2)区分干湿咳嗽，和3)评估咳嗽严重程度。首先,我们利用SSL专家知识聚合技术来克服数据集中的标签不一致性和标签稀疏性。接下来,我们的SSL方法用于识别重新标记的COUGHVID音频样本的子样本,该样本可用于训练或增强未来的咳嗽分类器.
结果：重新标记的COVID-19和健康数据的一致性证明，它表现出高度的类间特征可分性：比用户标记的数据高3倍。同样，SSL方法将咳嗽类型的可分性提高了11.3倍,严重程度分类的可分性提高了5.1倍.此外，用户标记的音频段中的频谱差异在重新标记的数据中被放大，导致健康咳嗽和COVID-19咳嗽在1-1.5kHz范围内的功率谱密度明显不同（p=1.2×10-64），从声学角度证明了新数据集的一致性及其可解释性。最后,我们演示了如何使用重新标记的数据集来训练COVID-19分类器，实现0.797的AUC。
结论：我们首次提出了一种针对咳嗽声音分类领域的SSL专家知识聚合技术，并演示如何以可解释的方式将多个专家的医学知识结合起来，从而提供丰富的，咳嗽分类任务的一致数据。
方法：在这项工作中，我们使用半监督学习(SSL)方法-基于音频信号处理工具和可解释的机器学习模型-提高COUGHVID数据集的标记一致性，用于1)COVID-19与健康咳嗽声音分类2)区分干湿咳嗽，和3)评估咳嗽严重程度。首先,我们利用SSL专家知识聚合技术来克服数据集中的标签不一致性和标签稀疏性。接下来,我们的SSL方法用于识别重新标记的COUGHVID音频样本的子样本,该样本可用于训练或增强未来的咳嗽分类器.
结果：重新标记的COVID-19和健康数据的一致性证明，它表现出高度的类间特征可分性：比用户标记的数据高3倍。同样，SSL方法将咳嗽类型的可分性提高了11.3倍,严重程度分类的可分性提高了5.1倍.此外，用户标记的音频段中的频谱差异在重新标记的数据中被放大，导致健康咳嗽和COVID-19咳嗽在1-1.5kHz范围内的功率谱密度明显不同（p=1.2×10-64），从声学角度证明了新数据集的一致性及其可解释性。最后,我们演示了如何使用重新标记的数据集来训练COVID-19分类器，实现0.797的AUC。
结论：我们首次提出了一种针对咳嗽声音分类领域的SSL专家知识聚合技术，并演示如何以可解释的方式将多个专家的医学知识结合起来，从而提供丰富的，咳嗽分类任务的一致数据。
