  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
