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.

翻译：本文提出了一种基于深度学习的机器声音异常检测模型，该任务通过分析机器声音来检测异常设备。通过大量实验，我们表明多种特征工程技术——包括伪音频、音频分段、数据增强、马氏距离及窄频率带——均能有效提升系统性能。在评估的各项技术中，窄频率带展现出显著影响。事实上，我们提出的聚焦窄频率带的模型在DCASE 2022任务2开发集基准数据集上优于DCASE基线。本文揭示的窄频率带重要作用，将启发机器声音异常检测领域的研究人员进一步探索并聚焦频率带的新型网络架构。