MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
We present a machine sound dataset to benchmark domain generalization
techniques for anomalous sound detection (ASD). Domain shifts are differences
in data distributions that can degrade the detection performance, and handling
them is a major issue for the application of ASD systems. While currently
available datasets for ASD tasks assume that occurrences of domain shifts are
known, in practice, they can be difficult to detect. To handle such domain
shifts, domain generalization techniques that perform well regardless of the
domains should be investigated. In this paper, we present the first ASD dataset
for the domain generalization techniques, called MIMII DG. The dataset consists
of five machine types and three domain shift scenarios for each machine type.
The dataset is dedicated to the domain generalization task with features such
as multiple different values for parameters that cause domain shifts and
introduction of domain shifts that can be difficult to detect, such as shifts
in the background noise. Experimental results using two baseline systems
indicate that the dataset reproduces domain shift scenarios and is useful for
benchmarking domain generalization techniques.