Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection

• URL: http://arxiv.org/abs/2603.13749v1
• Date: Sat, 14 Mar 2026 04:38:41 GMT
• Title: Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection
• Abstract summary: A common training-free anomalous sound detection pipeline temporally pools frame-level representations into a band-preserving feature vector and scores anomalies.<n>A single global neighbor forces all bands to share the same reference, increasing band-level mismatch.<n>We propose BEAM, which stores temporally pooled sub-band vectors in a memory bank, retrieves neighbors per sub-band, and uniformly aggregates scores to reduce normal-score variability and improve discriminability.
• Score: 11.328862535847785
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting subtle deviations in noisy acoustic environments is central to anomalous sound detection (ASD). A common training-free ASD pipeline temporally pools frame-level representations into a band-preserving feature vector and scores anomalies using a single nearest-neighbor match. However, this global matching can inflate normal-score variance through two effects. First, when normal sounds exhibit band-wise variability, a single global neighbor forces all bands to share the same reference, increasing band-level mismatch. Second, cosine-based matching is energy-coupled, allowing a few high-energy bands to dominate score computation under normal energy fluctuations and further increase variance. We propose BEAM, which stores temporally pooled sub-band vectors in a memory bank, retrieves neighbors per sub-band, and uniformly aggregates scores to reduce normal-score variability and improve discriminability. We further introduce a parameter-free adaptive fusion to better handle diverse temporal dynamics in sub-band responses. Experiments on multiple DCASE Task 2 benchmarks show strong performance without task-specific training, robustness to noise and domain shifts, and complementary gains when combined with encoder fine-tuning.

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