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In industrial applications, the early detection of malfunctioning factorymachinery is crucial. In this paper, we consider acoustic malfunction detectionvia transfer learning. Contrary to the majority of current approaches which arebased on deep autoencoders, we propose to extract features using neuralnetworks that were pretrained on the task of image classification. We then usethese features to train a variety of anomaly detection models and show thatthis improves results compared to convolutional autoencoders in recordings offour different factory machines in noisy environments. Moreover, we find thatfeatures extracted from ResNet based networks yield better results than thosefrom AlexNet and Squeezenet. In our setting, Gaussian Mixture Models andOne-Class Support Vector Machines achieve the best anomaly detectionperformance.

Acoustic Anomaly Detection for Machine Sounds based on Image Transfer Learning