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.