Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning
This work addresses early malfunction detection in industrial settings, but it is incremental as it applies existing transfer learning methods to a new domain.
The paper tackled acoustic anomaly detection for factory machinery by using transfer learning from image classification networks, achieving improved results over convolutional autoencoders on recordings from four machines in noisy environments.
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 convolutional 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.