Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning [article]

Robert Müller, Fabian Ritz, Steffen Illium, Claudia Linnhoff-Popien
2020 arXiv   pre-print
In industrial applications, the early detection of malfunctioning factory machinery is crucial. In this paper, we consider acoustic malfunction detection via transfer learning. Contrary to the majority of current approaches which are based on deep autoencoders, we propose to extract features using neural networks that were pretrained on the task of image classification. We then use these features to train a variety of anomaly detection models and show that this improves results compared to
more » ... lutional autoencoders in recordings of four different factory machines in noisy environments. Moreover, we find that features extracted from ResNet based networks yield better results than those from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and One-Class Support Vector Machines achieve the best anomaly detection performance.
arXiv:2006.03429v2 fatcat:comzhbsfbbeibirmotbenzxlzy