MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
This dataset addresses the challenge of domain generalization for anomalous sound detection in industrial settings, enabling better benchmarking of techniques to handle unseen domain shifts, but it is incremental as it builds on existing ASD datasets by adding domain shift features.
The authors tackled the problem of domain shifts degrading anomalous sound detection (ASD) performance by introducing MIMII DG, the first dataset for benchmarking domain generalization techniques in ASD, which includes five machine types and three domain shift scenarios per type, with experimental results showing it reproduces domain shifts and is useful for benchmarking.
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.