MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions

Description This dataset is a sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions (MIMII DUE). The dataset consists of normal and abnormal operating sounds of five different types of industrial machines, i.e., fans, gearboxes, pumps, slide rails, and valves. The data for each machine type includes six subsets called ``sections'', and each section roughly corresponds to a single product. Each section consists of data from two domains, called the source domain and the target domain, with different conditions such as operating speed and environmental noise. This dataset is a subset of the dataset for DCASE 2021 Challenge Task 2, so the dataset is entirely the same as data included in the development dataset and additional training dataset. For more information, please see this paper and the pages of the development dataset and the task description for DCASE 2021 Challenge Task 2. Baseline system Two simple baseline systems are available on the Github repositories [URL] and [URL]. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task. Conditions of use This dataset was made by Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Publication If you use this dataset, please cite the following paper: Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," arXiv preprint arXiv: 2105.02702, 2021. [URL] Feedback If there is any problem, please contact us: Ryo Tanabe, ryo.tanabe.rw.xk@hitachi.com Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.co