In this work, we propose a novel data augmentation method for clinical audio datasets based on a conditional Wasserstein Generative Adversarial Network with Gradient Penalty (cWGAN-GP), operating on log-mel spectrograms. To validate our method, we created a clinical audio dataset which was recorded in a real-world operating room during Total Hip Arthroplasty (THA) procedures and contains typical sounds which resemble the different phases of the intervention. We demonstrate the capability of the proposed method to generate realistic class-conditioned samples from the dataset distribution and show that training with the generated augmented samples outperforms classical audio augmentation methods in terms of classification performance. The performance was evaluated using a ResNet-18 classifier which shows a mean Macro F1-score improvement of 1.70% in a 5-fold cross validation experiment using the proposed augmentation method. Because clinical data is often expensive to acquire, the development of realistic and high-quality data augmentation methods is crucial to improve the robustness and generalization capabilities of learning-based algorithms which is especially important for safety-critical medical applications. Therefore, the proposed data augmentation method is an important step towards improving the data bottleneck for clinical audio-based machine learning systems.
翻译:在这项工作中,我们提出了一个基于有条件的Wasserstein Genemental Adversarial网络的临床音频数据集的新的数据增强方法,该方法以记录-mel光谱仪(cWGAN-GP)运行。为了验证我们的方法,我们创建了一个临床音频数据集,该数据集记录在一个真实的操作室里,在Total Hip Arthroploplasy (THA)程序期间,该数据集记录在一个真实的操作室里,其中的典型声音与干预的不同阶段相似。我们展示了拟议方法从数据集分布中生成现实的、有等级限制的样本的能力,并表明用所生成的扩增样品进行的培训在分类性能方面优于古典音频增强方法。我们使用ResNet-18的分类器对绩效进行了评估,显示在使用拟议增强方法进行的5倍交叉校验试验中,该功能平均为1.70%。由于临床数据往往非常昂贵,因此开发现实和高质量的数据增强方法对于提高基于学习的算法的稳健性和普及能力至关重要,这对于安全的医学应用来说,因此,拟议的数据增强方法对于改进数据瓶式系统来说是重要的临床学习是一个重要的一步。