arXiv：cs.LG· Simone Lugani, Christof Pichler, Hannes Wegleiter, Christoph Feilmayr, Rodolfo Zunino, Edoardo Ragusa·· 3 小时前
低误报率下的异常声音检测少样本自适应方法
Few-Shot Adaptation for Anomalous Sound Detection at Low False-Positive Rate
AI 导读
研究提出两步式异常声音检测方案：仅有正常音频时部署正则化无监督模型，首次机器故障后利用故障期间采集的异常样本进行少样本自适应。自适应将 AUPR 从 23.0 提升至 64.0，低误报率区域的 pAUROC 从 39.6 提升至 72.8；协方差正则化同时改善了无监督模型的预测精度和训练稳定性。
正文
Abstract:Reliable fault detection is crucial for industrial machinery in large-scale facilities, where unexpected failures can cause costly downtime and production interruptions. Although acoustic monitoring provides a scalable solution for machine condition monitoring, its deployment is often constrained by high false-positive rates. We propose a two-step approach to automatic anomalous sound detection: first, when only normal operating audio samples are available, a regularized unsupervised model is deployed; then, after the first machine fault, a few-shot adaptation strategy exploits the anomalous samples acquired during the fault to improve the system. This adaptation increases the area under the precision-recall curve (AUPR) from 23.0 to 64.0 and improves detection in the low false-positive-rate region, with the partial area under the receiver operating characteristic curve (pAUROC) increasing from 39.6 to 72.8. We also show that covariance regularization improves both prediction accuracy and training stability in the unsupervised model. These results provide a practical strategy for acoustic anomaly detection systems, enabling improved accuracy and reduced false alarms.
| Subjects: | Sound (cs.SD); Machine Learning (cs.LG); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2610.19893 [cs.SD] |
| (or arXiv:2610.19893v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2610.19893 arXiv-issued DOI via DataCite (pending registration) |
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| Journal reference: | 2026 IEEE International Conference on Omni Layer Intelligent Systems (COINS), 2026 |
| Related DOI: | https://doi.org/10.1109/COINS69995.2026.11715629
DOI(s) linking to related resources |
Submission history
From: Simone Lugani [view email]
[v1]
Wed, 14 Oct 2026 09:51:03 UTC (370 KB)
来源：arXiv：cs.LG · arxiv.org