Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection
Authors: Kevin Wilkinghoff, Gordon Wichern, Jonathan Le Roux, Zheng-Hua Tan
Organizations: Department of Electronic Systems, Aalborg University, Aalborg, Denmark · Pioneer Centre for Artificial Intelligence, Copenhagen, Denmark · Mitsubishi Electric Research Laboratories (MERL), Cambridge, MA, USA
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.
Figures & tables
Figure 1: Average sorted distances (left) and distance ratios (right) for BEATs embeddings of the “ToyCar” machine on the DCASE2025 dataset in the source and target domains. Pronounced distance jumps and low ratios mark cluster exits, which occur earlier in the target domain and reveal violations of locality under fixed neighborhood sizes.
Figure 2: Relative performance with respect to not applying ldn , shown as a function of the neighborhood size K . Values correspond to the ratio between the performance obtained with the respective normalization method and that obtained without ldn . Results are geometric means across all five evaluated datasets. For Direct- act , results are averaged over ten independent trials.