论文标题
MIMII DG：用于故障的工业机器调查和检查域泛化任务的声音数据集
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task
论文作者
论文摘要
我们提出一个机器声音数据集，以实现异常声音检测（ASD）的基准域概括技术。域移位是可以降低检测性能的数据分布的差异，而处理它们是ASD系统应用的主要问题。虽然当前用于ASD任务的数据集假设已知域移动的出现，但实际上，它们可能很难检测到。为了处理此类域移位，应研究域的概括域的概括技术。在本文中，我们介绍了第一个用于域概括技术的ASD数据集，称为MIMII DG。数据集由每种机器类型的五种机器类型和三个域移动方案组成。该数据集专用于域的概括任务，其特征，例如引起域移动的参数和引入域移动的多个不同值，这些域移动很难检测到，例如背景噪声中的偏移。使用两个基线系统的实验结果表明，数据集可再现域移动方案，对于基准测试域的概括技术很有用。
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.
