一个统一模型搞定多种设备异常声音检测，效果比现有方法更好。
UD-ASD: A Unified Diffusion Model for Anomalous Sound Detection
- 用轻量模块嵌入设备编号，指导扩散模型重建特定设备音频
- 在DCASE2022数据集上，AUC提升3.44%，pAUC提升2.52%
- 无需为每台设备单独训练，适合跨设备、多场景部署
异常声音检测（ASD）通过监测声音判断设备是否故障。现有方法仅能检测有限异常类型，泛化能力差，或需为每台设备单独建模。扩散模型具备强泛化能力，可条件生成特定数据。本文提出一种统一扩散模型，仅引入小规模模块：将音频转为log-Mel谱图后，轻量模块将设备ID嵌入条件向量，引导模型对特定设备进行重建；再利用高斯混合模型拟合重建误差分布。该模型可同时监控多种设备，通过跨域学习捕捉更本质的特征空间。在DCASE2022挑战赛任务2上，相比基线模型，本方法实现AUC提升3.44%、pAUC提升2.52%，验证了有效性。
原文摘要 · Abstract (English)
Anomalous Sound Detection (ASD) aims to determine whether faults have occurred by monitoring sounds. Existing methods detect a limited range of anomalies, exhibit poor generalization, or train a separate model for each machine. Diffusion models possess strong generalization and can generate specific data with condition guidance. We propose a unified diffusion model only with a small module. The audio is first transformed into log-Mel spectrograms. The lightweight module embeds machine IDs into condition embeddings, guiding the model to reconstruct data for specific machines. Then diffusion model reconstructs data with condition, using Gaussian Mixture Models to fit the distributions of reconstruction errors. Our unified model could monitor multiple machine types and learn more fundamental feature spaces with cross-domain learning. Experiments on DCASE2022 Challenge Task 2 show that our model achieves 3.44% AUC and 2.52% pAUC improvements over baseline, validating its effectiveness.
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