The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model

In this paper, we propose a deep learning-based model for Acoustic Anomaly Detection of Machines, the task of detecting abnormal machine conditions by analyzing the machine sound. By conducting extensive experiments, we indicate that multiple techniques of pseudo audios, audio segment, data augmentation, Mahalanobis distance, and narrow frequency bands, which mainly focus on audio feature engineering, are effective to enhance the system performance. Among the evaluating techniques, the narrow frequency bands presents a significant impact for the improvement. Indeed, our proposed model, which focuses on the narrow frequency bands, outperforms the DCASE baseline on the benchmark dataset of DCASE 2022 Task 2 Development set. The important role of the narrow frequency bands highlighted in this paper inspires the research community on the task of Acoustic Anomaly Detection of Machines to further investigate and propose novel network architectures that focus on these frequency bands.