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We present a machine sound dataset to benchmark domain generalizationtechniques for anomalous sound detection (ASD). Domain shifts are differencesin data distributions that can degrade the detection performance, and handlingthem is a major issue for the application of ASD systems. While currentlyavailable datasets for ASD tasks assume that occurrences of domain shifts areknown, in practice, they can be difficult to detect. To handle such domainshifts, domain generalization techniques that perform well regardless of thedomains should be investigated. In this paper, we present the first ASD datasetfor the domain generalization techniques, called MIMII DG. The dataset consistsof five machine types and three domain shift scenarios for each machine type.The dataset is dedicated to the domain generalization task with features suchas multiple different values for parameters that cause domain shifts andintroduction of domain shifts that can be difficult to detect, such as shiftsin the background noise. Experimental results using two baseline systemsindicate that the dataset reproduces domain shift scenarios and is useful forbenchmarking domain generalization techniques.

MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task