用生成模型合成咳嗽声，提升新冠检测准确率。
Sound Signal Synthesis with Auxiliary Classifier GAN, COVID-19 cough as an example
- 用ACGAN生成健康与新冠咳嗽的梅尔频谱图
- 数据增强后分类器准确率从72%提升至75%
- 适合医疗AI数据稀缺场景下的模型优化
人工智能在医疗领域快速发展。由于数据量需求大，新冠疫情凸显了训练模型的困难。为缓解数据稀缺问题，本文利用辅助分类器生成对抗网络（ACGAN）生成合成的咳嗽声梅尔频谱图，用于增强新冠检测模型的训练。首先在平衡的Coughvid数据子集上训练卷积神经网络（CNN），获得72%的测试准确率作为基线。随后，使用ACGAN条件生成健康与新冠咳嗽的合成频谱图，对CNN训练集进行数据增强，使测试准确率提升至75%。研究揭示了训练过程中的不稳定性，并提出应对策略。
原文摘要 · Abstract (English)
One of the fastest-growing domains in AI is healthcare. Given its importance, it has been the interest of many researchers to deploy ML models into the ever-demanding healthcare domain to aid doctors and increase accessibility. Delivering reliable models, however, demands a sizable amount of data, and the recent COVID-19 pandemic served as a reminder of the rampant and scary nature of healthcare that makes training models difficult. To alleviate such scarcity, many published works attempted to synthesize radiological cough data to train better COVID-19 detection models on the respective radiological data. To accommodate the time sensitivity expected during a pandemic, this work focuses on detecting COVID-19 through coughs using synthetic data to improve the accuracy of the classifier. The work begins by training a CNN on a balanced subset of the Coughvid dataset, establishing a baseline classification test accuracy of 72%. The paper demonstrates how an Auxiliary Classification GAN (ACGAN) may be trained to conditionally generate novel synthetic Mel Spectrograms of both healthy and COVID-19 coughs. These coughs are used to augment the training dataset of the CNN classifier, allowing it to reach a new test accuracy of 75%. The work highlights the expected messiness and inconsistency in training and offers insights into detecting and handling such shortcomings.
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