  In this paper, we introduce MIMII DUE, a new dataset for malfunctioning
industrial machine investigation and inspection with domain shifts due to
changes in operational and environmental conditions. Conventional methods for
anomalous sound detection face practical challenges because the distribution of
features changes between the training and operational phases (called domain
shift) due to various real-world factors. To check the robustness against
domain shifts, we need a dataset that actually includes domain shifts, but such
a dataset does not exist so far. The new dataset we created consists of the
normal and abnormal operating sounds of five different types of industrial
machines under two different operational/environmental conditions (source
domain and target domain) independent of normal/abnormal, with domain shifts
occurring between the two domains. Experimental results showed significant
performance differences between the source and target domains, indicating that
the dataset contains the domain shifts. These findings demonstrate that the
dataset will be helpful for checking the robustness against domain shifts. The
dataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freely
available for download at https://zenodo.org/record/4740355
