SDLGASMLJun 13, 2022

Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques

arXiv:2206.05876v2121 citationsh-index: 28
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This addresses the challenge of domain shifts in real-world applications of anomalous sound detection for machine monitoring, representing an incremental improvement over previous tasks by handling unknown domains.

The paper tackled the problem of domain shifts in unsupervised anomalous sound detection for machine condition monitoring by focusing on domain generalization techniques, where the domain of test samples is unknown and only one threshold is allowed across domains, and analysis of 81 submissions revealed two main approaches: domain-mixing-based and domain-classification-based methods.

We present the task description and discussion on the results of the DCASE 2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) for machine condition monitoring applying domain generalization techniques''. Domain shifts are a critical problem for the application of ASD systems. Because domain shifts can change the acoustic characteristics of data, a model trained in a source domain performs poorly for a target domain. In DCASE 2021 Challenge Task 2, we organized an ASD task for handling domain shifts. In this task, it was assumed that the occurrences of domain shifts are known. However, in practice, the domain of each sample may not be given, and the domain shifts can occur implicitly. In 2022 Task 2, we focus on domain generalization techniques that detects anomalies regardless of the domain shifts. Specifically, the domain of each sample is not given in the test data and only one threshold is allowed for all domains. Analysis of 81 submissions from 31 teams revealed two remarkable types of domain generalization techniques: 1) domain-mixing-based approach that obtains generalized representations and 2) domain-classification-based approach that explicitly or implicitly classifies different domains to improve detection performance for each domain.

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