MIMII DUE：由于操作和环境条件的变化而引起的域偏移的故障工业机器调查和检查的声音数据集
MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions
摘要：
在本文中，我们介绍了MIMII DUE，一个新的数据集故障工业机器的调查和检查与域的变化，由于操作和环境条件的变化。用于异常声音检测的常规方法面临实际挑战，因为由于各种现实因素，特征的分布在训练和操作阶段之间变化（称为域移位）。为了检查对域偏移的鲁棒性，我们需要一个实际包含域偏移的数据集，但到目前为止还不存在这样的数据集。我们创建的新数据集由五种不同类型的工业机器在两种不同的操作/环境条件（源域和目标域）下的正常和异常操作声音组成，独立于正常/异常，两个域之间发生域转移。实验结果表明，源域和目标域之间存在显著的性能差异，表明数据集包含域偏移。这些发现表明，该数据集将有助于检查对域移位的鲁棒性。该数据集是DCASE 2021挑战任务2数据集的子集，可在www.example.com免费下载
英文摘要：
In this paper, we introduce MIMII DUE, a new dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions. Conventional methods for anomalous sound detection face practical challenges because the distribution of features changes between the training and operational phases (called domain shift) due to various real-world factors. To check the robustness against domain shifts, we need a dataset that actually includes domain shifts, but such a dataset does not exist so far. The new dataset we created consists of the normal and abnormal operating sounds of five different types of industrial machines under two different operational/environmental conditions (source domain and target domain) independent of normal/abnormal, with domain shifts occurring between the two domains. Experimental results showed significant performance differences between the source and target domains, indicating that the dataset contains the domain shifts. These findings demonstrate that the dataset will be helpful for checking the robustness against domain shifts. The dataset is a subset of the dataset for DCASE 2021 Challenge Task 2 and freely available for download at https://zenodo.org/record/4740355
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
