16‐2: Machine‐Anomaly Sound Detection Using Convolutional Recurrent Neural Network with Prediction Loss

Abstract

Automatic machine anomaly sound detection is important for machine maintenance in display manufacturing factory. Recently, unsupervised anomaly detection approach based on autoencoder and reconstruction error has been proposed, which has assumption that the network trained only with the normal data produces higher reconstruction errors for the anomalies than the normal inputs. However, most approaches based on autoencoder consist of Convolutional Neural Network (CNN) which deals with the spectrogram of the sound like an image. In this paper, we propose Convolutional Recurrent Neural Network to use the characteristics of the spectrogram. To effectively integrate time and frequency information, we apply a 1‐dimensional convolutional layers and recurrent neural network to the network structure. Also, we introduce a prediction loss to prevent the network from learning to reconstruct anomalies well. Experimental results on the ToyADMOS and MIMII datasets demonstrate that the proposed approach achieves promising performance.