相对失配：用于异常声音检测的特征空间流的局部参考校准
Relative Mismatch: Local-Reference Calibration of Feature-Space Flows for Anomalous Sound Detection
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中文总结 AI 辅助
本文提出相对失配方法，利用流匹配生成模型进行异常声音检测，通过局部参考校准失配，在DCASE 2020--2025上取得71.01的最高分，展现鲁棒性与稳定性。
中文摘要 AI 辅助
异常声音检测（ASD）长期以来一直由基于k近邻（KNN）的检测器主导，这些检测器本质上是在正常样本上进行隐式似然估计。在本工作中，我们研究生成模型是否能更好地承担这一角色。我们提出了相对失配（Relative Mismatch），一种由流匹配驱动的生成式ASD后端，它学习一个速度场，将高斯噪声传输到正常性的代表性特征空间。在推理时，它测量真实路径速度与预测路径速度之间的失配，并通过两级设计进行聚合。为了减轻由域偏移引起的固有失配偏移，每个查询进一步通过其局部正常参考的失配进行校准，从而仅暴露其超出正常性的偏差。在DCASE 2020--2025上的大量实验表明，相对失配以71.01的最高分数超越了最先进的后端，同时具有强大的鲁棒性和训练稳定性。此外，我们表明，策划一个紧凑且具有判别性的特征空间是释放生成模型在ASD中潜力的关键。
英文摘要
Anomalous sound detection (ASD) has long been dominated by k-nearest-neighbor (KNN) based detectors, which essentially perform implicit likelihood estimation over normal samples. In this work, we investigate whether generative models can better serve this role. We propose Relative Mismatch, a generative ASD backend powered by flow matching, which learns a velocity field that transports Gaussian noise to a representative feature space of normality. During inference, it measures the mismatch between the oracle and predicted path velocities and aggregates them through a two-level design. To mitigate the inherent mismatch offsets incurred by domain shift, each query is further calibrated with the mismatch of its local normal reference, thereby exposing only its deviation beyond normality. Extensive experiments on DCASE 2020--2025 demonstrate that Relative Mismatch outperforms state-of-the-art backends with the highest score of 71.01, along with strong robustness and training stability. Furthermore, we show that curating a compact and discriminative feature space is the key to unleash the power of generative models for ASD.
发表机构
- Tsinghua University（清华大学）
- Shanghai Jiao Tong University（上海交通大学）
- North China Electric Power University（华北电力大学）
- Huakong AI Plus（华控智加）
机构由 AI 辅助整理，请以论文原文为准。
