动态调整邻域大小，让异常声音检测更稳定可靠。
Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection
- 通过检测聚类边界突变自动选最优邻域大小
- 在多个数据集上显著提升异常检测准确率
- 适合对鲁棒性要求高的音频异常检测场景
基于局部密度的得分归一化是距离嵌入方法中用于异常声音检测的有效组件，尤其在不同条件或领域间数据密度变化时表现良好。然而实际性能高度依赖邻域大小：当邻域扩大跨越聚类边界时，会破坏局部密度估计的局部性假设，导致检测精度下降。为此，我们提出聚类退出检测机制，通过识别距离不连续性来动态选择邻域大小，实现基于局部性保持的自适应调整。在多种嵌入模型和数据集上的实验表明，该方法显著提升了对邻域大小选择的鲁棒性，并带来一致的性能提升。
原文摘要 · Abstract (English)
Local density-based score normalization is an effective component of distance-based embedding methods for anomalous sound detection, particularly when data densities vary across conditions or domains. In practice, however, performance depends strongly on neighborhood size. Increasing it can degrade detection accuracy when neighborhood expansion crosses cluster boundaries, violating the locality assumption of local density estimation. This observation motivates adapting the neighborhood size based on locality preservation rather than fixing it in advance. We realize this by proposing cluster exit detection, a lightweight mechanism that identifies distance discontinuities and selects neighborhood sizes accordingly. Experiments across multiple embedding models and datasets show improved robustness to neighborhood-size selection and consistent performance gains.
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