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We present the task description and discussion on the results of the DCASE2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sounddetection (ASD) task, identifying whether a given sound was normal or anomalouswithout anomalous training data. In 2021, we organized an advanced unsupervisedASD task under domain-shift conditions, which focuses on the inevitable problemof the practical use of ASD systems. The main challenge of this task is todetect unknown anomalous sounds where the acoustic characteristics of thetraining and testing samples are different, i.e., domain-shifted. This problemfrequently occurs due to changes in seasons, manufactured products, and/orenvironmental noise. We received 75 submissions from 26 teams, and severalnovel approaches have been developed in this challenge. On the basis of theanalysis of the evaluation results, we found that there are two types ofremarkable approaches that TOP-5 winning teams adopted: 1) ensemble approachesof ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-baseddetectors and 2) approaches based on IM-based detection for features learned ina machine-identification task.

Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised  Anomalous Sound Detection for Machine Condition Monitoring under Domain  Shifted Conditions