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

arXiv: https://arxiv.org/abs/2105.02702 · HTML: https://mlsanity.com/paper/2105.02702/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 6 May 2021.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Pyroomacoustics: A Python package for audio room simulations and array processing algorithms](https://mlsanity.com/paper/1710.04196/) (2017)
- [A multi-device dataset for urban acoustic scene classification](https://mlsanity.com/paper/1807.09840/) (2018)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [Acoustic scene classification in DCASE 2020 Challenge: generalization across devices and low complexity solutions](https://mlsanity.com/paper/2005.14623/) (2020)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2006.05822/) (2020)
- [Anomalous sound detection based on interpolation deep neural network](https://mlsanity.com/paper/2005.09234/) (2020)
- [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018)
- [Deep Autoencoding GMM-based Unsupervised Anomaly Detection in Acoustic Signals and its Hyper-parameter Optimization](https://mlsanity.com/paper/2009.12042/) (2020)
- [Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples](https://mlsanity.com/paper/2011.02949/) (2020)
- [Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds](https://mlsanity.com/paper/1907.08338/) (2019)
- [ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection](https://mlsanity.com/paper/2007.05314/) (2020)

## Built on by (most cited first, 9 of 9)

- [MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task](https://mlsanity.com/paper/2205.13879/) (2022)
- [Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions](https://mlsanity.com/paper/2106.04492/) (2021)
- [SciTS: Scientific Time Series Understanding and Generation with LLMs](https://mlsanity.com/paper/2510.03255/) (2025)
- [Multimedia Datasets for Anomaly Detection: A Review](https://mlsanity.com/paper/2112.05410/) (2021)
- [Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detection](https://mlsanity.com/paper/2204.01905/) (2022)
- [Anomalous Sound Detection Based on Machine Activity Detection](https://mlsanity.com/paper/2204.07353/) (2022)
- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)
- [Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors](https://mlsanity.com/paper/2211.01716/) (2022)
- [MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection](https://mlsanity.com/paper/2507.20666/) (2025)

## Abstract (arXiv)

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