Automatic detection of machine anomaly remains challenging for machine learning. We believe the capability of generative adversarial network (GAN) suits the need of machine audio anomaly detection, yet rarely has this been investigated by previous work. In this paper, we propose AEGAN-AD, a totally unsupervised approach in which the generator (also an autoencoder) is trained to reconstruct input spectrograms. It is pointed out that the denoising nature of reconstruction deprecates its capacity. Thus, the discriminator is redesigned to aid the generator during both training stage and detection stage. The performance of AEGAN-AD on the dataset of DCASE 2022 Challenge TASK 2 demonstrates the state-of-the-art result on five machine types. A novel anomaly localization method is also investigated. Source code available at: www.github.com/jianganbai/AEGAN-AD
翻译:自动检测机器异常在机器学习中仍然具有挑战性。我们认为生成对抗网络(GAN)的能力适用于机器音频异常检测,但此前的工作很少对此进行研究。在本文中,我们提出了一种完全无监督的方法AEGAN-AD,其中生成器(也是自编码器)被训练用于重构输入的频谱图。指出重构的去噪特性降低了其能力。因此,判别器被重新设计以在训练阶段和检测阶段协助生成器。 AEGAN-AD在DCASE 2022 Challenge TASK 2数据集上的表现证明了其在五种机器类型上的最先进结果。还调查了一种新的异常定位方法。源代码可在以下链接获得:www.github.com/jianganbai/AEGAN-AD。