The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model
arxiv(2024)
摘要
In this paper, we propose a deep learning based model for Acoustic Anomaly
Detection of Machines, the task for detecting abnormal machines by analysing
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 feature
engineering, are effective to enhance the system performance. Among the
evaluating techniques, the narrow frequency bands presents a significant
impact. 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
indicated in this paper inspires the research community on the task of Acoustic
Anomaly Detection of Machines to further investigate and propose novel network
architectures focusing on the frequency bands.
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