用扩散模型检测工厂异常声音，效果优于基线7.75%。
ASD-Diffusion: Anomalous Sound Detection with Diffusion Models
- 通过噪声重建声学特征，还原正常模式
- 重构后偏差大的声音被判定为异常，提升检测精度
- 采用去噪扩散隐式模型加速推理，适合工业实时场景
无监督异常声音检测（ASD）旨在仅使用正常声音数据时，设计可泛化的异常检测方法。本文提出基于扩散模型的异常声音检测方法（ASD-Diffusion），用于真实工厂环境中的声音异常检测。在该方法中，通过将含噪声的声学特征逐步去噪，重建为近似正常的模式；随后引入后处理异常过滤算法，识别重建后与原始输入差异显著的声音片段作为异常。此外，采用去噪扩散隐式模型（denoising diffusion implicit model），通过延长去噪过程的采样间隔，显著提升推理速度。在DCASE 2023挑战赛任务2的开发集上，该方法相比基线性能提升7.75%，验证了其有效性。
原文摘要 · Abstract (English)
Unsupervised Anomalous Sound Detection (ASD) aims to design a generalizable method that can be used to detect anomalies when only normal sounds are given. In this paper, Anomalous Sound Detection based on Diffusion Models (ASD-Diffusion) is proposed for ASD in real-world factories. In our pipeline, the anomalies in acoustic features are reconstructed from their noisy corrupted features into their approximate normal pattern. Secondly, a post-processing anomalies filter algorithm is proposed to detect anomalies that exhibit significant deviation from the original input after reconstruction. Furthermore, denoising diffusion implicit model is introduced to accelerate the inference speed by a longer sampling interval of the denoising process. The proposed method is innovative in the application of diffusion models as a new scheme. Experimental results on the development set of DCASE 2023 challenge task 2 outperform the baseline by 7.75%, demonstrating the effectiveness of the proposed method.
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。