# MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection

arXiv: https://arxiv.org/abs/1909.09347 · HTML: https://mlsanity.com/paper/1909.09347/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 30 of 58)

- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series](https://mlsanity.com/paper/2105.00899/) (2021)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task](https://mlsanity.com/paper/2205.13879/) (2022)
- [DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection](https://mlsanity.com/paper/2106.05410/) (2021)
- [Energy-Based Models for Anomaly Detection: A Manifold Diffusion Recovery Approach](https://mlsanity.com/paper/2310.18677/) (2023)
- [MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI](https://mlsanity.com/paper/2410.12032/) (2024)
- [MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions](https://mlsanity.com/paper/2105.02702/) (2021)
- [OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection](https://mlsanity.com/paper/2104.00528/) (2021)
- [Bandit Quickest Changepoint Detection](https://mlsanity.com/paper/2107.10492/) (2021)
- [Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining](https://mlsanity.com/paper/2304.03588/) (2023)
- [Anomalous Sound Detection with Machine Learning: A Systematic Review](https://mlsanity.com/paper/2102.07820/) (2021)
- [Active multi-fidelity Bayesian online changepoint detection](https://mlsanity.com/paper/2103.14224/) (2021)
- [Learnable Wavelet Packet Transform for Data-Adapted Spectrograms](https://mlsanity.com/paper/2201.11069/) (2022)
- [Semi-Supervised Health Index Monitoring with Feature Generation and Fusion](https://mlsanity.com/paper/2312.02867/) (2023)
- [Deep Generic Representations for Domain-Generalized Anomalous Sound Detection](https://mlsanity.com/paper/2409.05035/) (2024)
- [A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges](https://mlsanity.com/paper/2501.15196/) (2025)
- [Canonical Polyadic Decomposition and Deep Learning for Machine Fault Detection](https://mlsanity.com/paper/2107.09519/) (2021)
- [Noisy-ArcMix: Additive Noisy Angular Margin Loss Combined With Mixup Anomalous Sound Detection](https://mlsanity.com/paper/2310.06364/) (2023)
- [SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases](https://mlsanity.com/paper/2603.09853/) (2026)
- [Robust Audio Anomaly Detection](https://mlsanity.com/paper/2202.01784/) (2022)
- [Hierarchical Conditional Variational Autoencoder Based Acoustic Anomaly Detection](https://mlsanity.com/paper/2206.05460/) (2022)
- [Machine Anomalous Sound Detection Using Spectral-temporal Modulation Representations Derived from Machine-specific Filterbanks](https://mlsanity.com/paper/2409.05319/) (2024)
- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)
- [Anomalous Sound Detection Using a Binary Classification Model and Class Centroids](https://mlsanity.com/paper/2106.06151/) (2021)
- [Health Monitoring of Industrial machines using Scene-Aware Threshold Selection](https://mlsanity.com/paper/2111.10897/) (2021)
- [Acoustic Signal Analysis with Deep Neural Network for Detecting Fault Diagnosis in Industrial Machines](https://mlsanity.com/paper/2312.01062/) (2023)
- [Low-Power Vibration-Based Predictive Maintenance for Industry 4.0 using Neural Networks: A Survey](https://mlsanity.com/paper/2408.00516/) (2024)
- [Audio-based Anomaly Detection in Industrial Machines Using Deep One-Class Support Vector Data Description](https://mlsanity.com/paper/2412.10792/) (2024)
- [From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based Algorithms](https://mlsanity.com/paper/2502.18328/) (2025)

## Abstract (arXiv)

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