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We present the task description and discussion on the results of the DCASE2022 Challenge Task 2: ``Unsupervised anomalous sound detection (ASD) formachine 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 modeltrained in a source domain performs poorly for a target domain. In DCASE 2021Challenge Task 2, we organized an ASD task for handling domain shifts. In thistask, 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 shiftscan occur implicitly. In 2022 Task 2, we focus on domain generalizationtechniques that detects anomalies regardless of the domain shifts.Specifically, the domain of each sample is not given in the test data and onlyone threshold is allowed for all domains. Analysis of 81 submissions from 31teams 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 classifiesdifferent domains to improve detection performance for each domain.

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