Factory machinery is prone to failure or breakdown, resulting in significant expenses for companies. Hence, there is a rising interest in machine monitoring using different sensors including microphones. In the scientific community, the emergence of public datasets has led to advancements in acoustic detection and classification of scenes and events, but there are no public datasets that focus on the sound of industrial machines under normal and anomalous operating conditions in real factory environments. In this paper, we present a new dataset of industrial machine sounds that we call a sound dataset for malfunctioning industrial machine investigation and inspection (MIMII dataset). Normal sounds were recorded for different types of industrial machines (i.e., valves, pumps, fans, and slide rails), and to resemble a real-life scenario, various anomalous sounds were recorded (e.g., contamination, leakage, rotating unbalance, and rail damage). The purpose of releasing the MIMII dataset is to assist the machine-learning and signal-processing community with their development of automated facility maintenance. The MIMII dataset is freely available for download at: https://zenodo.org/record/3384388

翻译：在科学界,公共数据集的出现导致声学探测和对场景和事件进行分类的进展,但是没有公共数据集以工业机器正常和异常操作条件下的实际工厂环境中的工业机器声音为重点(例如污染、渗漏、旋转不平衡和铁路损坏),释放MIMII数据集的目的是协助机器学习和信号处理社区发展自动化设施维护。 MIMII数据集可以免费下载:https://zenodo.org/record/33888。