多尺度扫描网络提升机器异常声音检测准确率
Multi-scale Scanning Network for Machine Anomalous Sound Detection
- 用不同尺寸卷积核扫描声谱图，捕捉多尺度时频模式
- 在DCASE 2020和2023数据集上达到最新最优性能
- 适合需要高精度工业声音异常检测的场景
机器声音在频率和时间域中具有稳定且重复的模式，但不同机器类型在不同尺度上的表现差异显著。例如，旋转类机器在短时间间隔内呈现周期性特征，而往复类机器则表现出跨越较长时间域的广域模式。尽管已有研究利用这些模式提升异常声音检测（ASD）效果，但跨尺度模式的变化仍缺乏充分探索。为此，本文提出多尺度扫描网络（MSN），通过不同尺寸的核盒扫描音频声谱图，并结合共享权重的轻量级卷积网络，实现高效可扩展的特征表示。在DCASE 2020和DCASE 2023任务2数据集上的实验表明，MSN达到当前最优性能，验证了其在提升ASD系统有效性方面的潜力。
原文摘要 · Abstract (English)
Machine sounds exhibit consistent and repetitive patterns in both the frequency and time domains, which vary significantly across scales for different machine types. For instance, rotating machines often show periodic features in short time intervals, while reciprocating machines exhibit broader patterns spanning the time domain. While prior studies have leveraged these patterns to improve Anomalous Sound Detection (ASD), the variation of patterns across scales remains insufficiently explored. To address this gap, we introduce a Multi-scale Scanning Network (MSN) designed to capture patterns at multiple scales. MSN employs kernel boxes of varying sizes to scan audio spectrograms and integrates a lightweight convolutional network with shared weights for efficient and scalable feature representation. Experimental evaluations on the DCASE 2020 and DCASE 2023 Task 2 datasets demonstrate that MSN achieves state-of-the-art performance, highlighting its effectiveness in advancing ASD systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。