  We present the task description and discussion on the results of the DCASE
2021 Challenge Task 2. In 2020, we organized an unsupervised anomalous sound
detection (ASD) task, identifying whether a given sound was normal or anomalous
without anomalous training data. In 2021, we organized an advanced unsupervised
ASD task under domain-shift conditions, which focuses on the inevitable problem
of the practical use of ASD systems. The main challenge of this task is to
detect unknown anomalous sounds where the acoustic characteristics of the
training and testing samples are different, i.e., domain-shifted. This problem
frequently occurs due to changes in seasons, manufactured products, and/or
environmental noise. We received 75 submissions from 26 teams, and several
novel approaches have been developed in this challenge. On the basis of the
analysis of the evaluation results, we found that there are two types of
remarkable approaches that TOP-5 winning teams adopted: 1) ensemble approaches
of ``outlier exposure'' (OE)-based detectors and ``inlier modeling'' (IM)-based
detectors and 2) approaches based on IM-based detection for features learned in
a machine-identification task.
