Anomaly detection and visualization for sound data with pre-trained deep neural networks

Abstract

Anomaly detection in the industrial domain is a critical issue. Although anomaly detection needs expert's experiences or huge person-hours, recent researches of deep neural networks can save much labor. This paper proposes an anomaly detection method for sound data with pre-trained deep neural networks, and an anomaly visualization method to specify the frequency components of anomalous sounds. Common anomaly detection methods train deep neural networks which can detect anomalies in specific domains. However, training such networks from scratch costs a lot because the neural networks have numerous parameters and use lots of data to improve the performance. To address this problem, anomaly detection using the pre-trained image classification networks has been proposed. This method uses feature representations of classification networks and the Mahalanobis distance as the anomaly scores. In this paper, we propose a method which can apply the above method to sound data and evaluate its performance with a dataset for industrial sound which contains machine sounds under normal and anomalous operating conditions in real factory environments. We also propose a visualization method to specify the frequency components when abnormal sounds occur.