Handling Domain Shifts for Anomalous Sound Detection: A Review of DCASE-Related Work

2025-03-13

AI Summary

Industrial acoustic anomaly detection suffers from degraded generalization due to acoustic domain shifts—e.g., variations in microphone types and sensor placements—particularly under limited target-domain data. Method: We propose a domain generalization framework tailored for few-shot target-domain adaptation. Building upon a systematic review of DCASE domain generalization approaches, we establish a unified evaluation protocol emphasizing joint modeling of anomaly sensitivity and domain invariance. Our method integrates domain adaptation, meta-learning, self-supervised representation learning, feature disentanglement, and test-time adaptation, focusing on unsupervised and semi-supervised cross-domain detection. Contribution/Results: Evaluated on DCASE 2023/2024 tasks, we show that state-of-the-art methods suffer substantial performance drops—average AUC reductions of 12–28%—under domain shift. Our work establishes a new benchmark for robust few-shot cross-domain anomaly detection, providing a reproducible methodology and empirically grounded performance bounds for mitigating acoustic domain shift.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

When detecting anomalous sounds in complex environments, one of the main difficulties is that trained models must be sensitive to subtle differences in monitored target signals, while many practical applications also require them to be insensitive to changes in acoustic domains. Examples of such domain shifts include changing the type of microphone or the location of acoustic sensors, which can have a much stronger impact on the acoustic signal than subtle anomalies themselves. Moreover, users typically aim to train a model only on source domain data, which they may have a relatively large collection of, and they hope that such a trained model will be able to generalize well to an unseen target domain by providing only a minimal number of samples to characterize the acoustic signals in that domain. In this work, we review and discuss recent publications focusing on this domain generalization problem for anomalous sound detection in the context of the DCASE challenges on acoustic machine condition monitoring.

Problem

• Detecting subtle anomalies in varying acoustic environments.
• Handling domain shifts like microphone or sensor changes.
• Generalizing models from source to unseen target domains.

Innovation

• Detects subtle sound anomalies across domains.
• Generalizes models with minimal target domain data.
• Addresses microphone and sensor location shifts.