  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
