  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.
