A problem-oriented survey of anomalous sound detection: From normality learning to deployable acoustic intelligence

Anomalous sound detection (ASD) identifies deviations from learned normal acoustic patterns and is increasingly used in intelligent acoustic sensing for industrial monitoring, medical diagnosis, energy systems, transportation safety, urban sensing, and smart infrastructure. Recent benchmarks and evaluation protocols have shifted ASD from normal-only machine-sound detection toward domain shift, domain generalization, first-shot deployment, and multi-sensor monitoring. This review provides a problem-oriented survey that connects dataset evolution, evaluation metrics, machine-sound properties, methodological paradigms, and deployment requirements. We summarize representative benchmarks and related acoustic anomaly datasets, review benchmark- and deployment-oriented evaluation criteria, and organize ASD methods into five paradigms: reconstruction-based normality learning, embedding structuring learning, statistical scoring and calibration, robust fusion and evidence integration, and first-shot and deployment adaptation. The taxonomy clarifies how anomaly scarcity, domain variation, score calibration, heterogeneous sensing, and deployment constraints shape method design. We further discuss open challenges related to limited anomalous data, score instability, incomplete metadata, edge computation, reproducibility, and interpretability. Overall, ASD is moving from benchmark-centered anomaly detection toward deployable acoustic intelligence, where robust representations, calibrated decisions, efficient implementation, multi-sensor evidence alignment, and actionable explanations are essential for reliable real-world monitoring.