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In this paper, we introduce MIMII DUE, a new dataset for malfunctioningindustrial machine investigation and inspection with domain shifts due tochanges in operational and environmental conditions. Conventional methods foranomalous sound detection face practical challenges because the distribution offeatures changes between the training and operational phases (called domainshift) due to various real-world factors. To check the robustness againstdomain shifts, we need a dataset that actually includes domain shifts, but sucha dataset does not exist so far. The new dataset we created consists of thenormal and abnormal operating sounds of five different types of industrialmachines under two different operational/environmental conditions (sourcedomain and target domain) independent of normal/abnormal, with domain shiftsoccurring between the two domains. Experimental results showed significantperformance differences between the source and target domains, indicating thatthe dataset contains the domain shifts. These findings demonstrate that thedataset will be helpful for checking the robustness against domain shifts. Thedataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freelyavailable for download at https://zenodo.org/record/4740355

MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine  Investigation and Inspection with Domain Shifts due to Changes in Operational  and Environmental Conditions