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

Tin Nguyen; Lam Pham; Phat Lam; Dat Ngo; Hieu Tang; Alexander Schindler

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arXiv · 2403.00379

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

Tin Nguyen·Lam Pham·Phat Lam·Dat Ngo·Hieu Tang·Alexander Schindler

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Abstract

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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Tin Nguyen, Lam Pham, Phat Lam, Dat Ngo, Hieu Tang, Alexander Schindler. 2024-03-01. The Impact of Frequency Bands on Acoustic Anomaly Detection of Machines using Deep Learning Based Model. https://arxiv.org/abs/2403.00379

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