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.

翻译：局部密度分数归一化是实现基于距离的异常声音检测嵌入方法的有效组成部分，特别是在数据密度随条件或领域变化时尤为关键。然而在实践中，其性能高度依赖于邻域尺寸。当邻域扩张跨越聚类边界时，增加邻域尺寸可能降低检测精度，从而违背局部密度估计的局部性假设。这一发现启发我们基于局部性保持来动态调整邻域尺寸，而非预先固定该参数。为此，我们提出聚类退出检测机制——一种轻量级方法，通过识别距离不连续点并据此选择邻域尺寸。在多种嵌入模型和数据集上的实验表明，该方法增强了对邻域尺寸选择的鲁棒性，并持续带来性能提升。