Abstract:

Anomaly detection systems are increasingly embedded in real-world monitoring pipelines, yet they typically produce only a scalar score, leaving operators without the context needed to act. We propose an explanation framework that attributes anomaly scores to interpretable acoustic features. Given an anomalous sample, a trained sparse autoencoder decomposes its anomaly score into per-feature contributions; these features are then linked to semantic attributes via linear probes trained on machine operating condition metadata and validated through counterfactual interventions in the latent space. We demonstrate the approach for unsupervised machine sound anomaly detection on the DCASE2025 Task 2 benchmark, achieving performance comparable to state-of-the-art detection systems and providing human- interpretable explanations of anomalous behaviour.