[4N1-GS-7] Vision, speech media processing:

[4N1-GS-7-03] A Study on the Generation of Pseudo-Anomalous Data for Anomaly Detection in Machine Sounds

〇Satoshi Kawamura¹, Kohei Yamamoto¹, Hideaki Tamai¹ (1. Oki Electric Industry Co., Ltd.)

Keywords:sound, anomaly detection, pseudo anomaly, data augmentation, deep learning

The detection of anomalous sounds is crucial for the efficient operation and maintenance of factory machinery. However, obtaining real-world anomalous data is often impractical, limiting the training of anomaly detection models. To address this issue, classification-based approaches utilizing pseudo-anomalous data have garnered significant interest. Pseudo-anomalies can be generated by treating normal sounds from non-target machines as anomalies or by applying perturbations to normal sounds using neural networks. While the former approach necessitates machine-specific models, the latter may fail to adequately capture the unique characteristics of target machines. This study investigates various sound data augmentation techniques for generating pseudo-anomalies and systematically evaluates their combinations to improve anomaly detection performance. Experimental results demonstrate that certain augmentations substantially enhance detection accuracy. Furthermore, we analyze the individual impact of each method and discuss the influence of augmented sound data on anomaly detection models.