# MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Task

arXiv: https://arxiv.org/abs/2205.13879 · HTML: https://mlsanity.com/paper/2205.13879/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 16 of 16)

- [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)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [Heterogeneous Domain Generalization via Domain Mixup](https://mlsanity.com/paper/2009.05448/) (2020)
- [MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection](https://mlsanity.com/paper/1909.09347/) (2019)
- [ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection](https://mlsanity.com/paper/1908.03299/) (2019)
- [Anomalous sound detection based on interpolation deep neural network](https://mlsanity.com/paper/2005.09234/) (2020)
- [ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions](https://mlsanity.com/paper/2106.02369/) (2021)
- [Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma](https://mlsanity.com/paper/1810.09133/) (2018)
- [Flow-based Self-supervised Density Estimation for Anomalous Sound Detection](https://mlsanity.com/paper/2103.08801/) (2021)
- [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)
- [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)
- [Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds](https://mlsanity.com/paper/1907.08338/) (2019)
- [Domain Generalization in Biosignal Classification](https://mlsanity.com/paper/2011.06207/) (2020)
- [Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts](https://mlsanity.com/paper/2111.06539/) (2021)

## Built on by (most cited first, 23 of 23)

- [Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques](https://mlsanity.com/paper/2206.05876/) (2022)
- [Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2305.07828/) (2023)
- [Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring](https://mlsanity.com/paper/2406.07250/) (2024)
- [Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approach](https://mlsanity.com/paper/2303.17949/) (2023)
- [Self-Supervised Learning for Anomalous Sound Detection](https://mlsanity.com/paper/2312.09578/) (2023)
- [Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?](https://mlsanity.com/paper/2309.15643/) (2023)
- [Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Models](https://mlsanity.com/paper/2409.07016/) (2024)
- [ASD-Diffusion: Anomalous Sound Detection with Diffusion Models](https://mlsanity.com/paper/2409.15957/) (2024)
- [FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representation](https://mlsanity.com/paper/2507.16696/) (2025)
- [MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System](https://mlsanity.com/paper/2409.18542/) (2024)
- [AP-OOD: Attention Pooling for Out-of-Distribution Detection](https://mlsanity.com/paper/2602.06031/) (2026)
- [Analysis-Driven Procedural Generation of an Engine Sound Dataset with Embedded Control Annotations](https://mlsanity.com/paper/2603.07584/) (2026)
- [False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control](https://mlsanity.com/paper/2606.15153/) (2026)
- [Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?](https://mlsanity.com/paper/2609.26460/) (2026)
- [Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection](https://mlsanity.com/paper/2410.03904/) (2024)
- [MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detection](https://mlsanity.com/paper/2507.20666/) (2025)
- [Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-training](https://mlsanity.com/paper/2509.12845/) (2025)
- [A Framework for Evaluating Faithfulness in Explainable AI for Machine Anomalous Sound Detection Using Frequency-Band Perturbation](https://mlsanity.com/paper/2601.19017/) (2026)
- [Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection](https://mlsanity.com/paper/2603.13749/) (2026)
- [Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detection](https://mlsanity.com/paper/2607.04526/) (2026)
- [UD-ASD: A Unified Diffusion Model for Anomalous Sound Detection](https://mlsanity.com/paper/2607.12576/) (2026)
- [Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data](https://mlsanity.com/paper/2608.25794/) (2026)
- [Anomaly-Free Self-Optimization via AUC Bounds](https://mlsanity.com/paper/2609.27362/) (2026)

## Abstract (arXiv)

We present a machine sound dataset to benchmark domain generalization techniques for anomalous sound detection (ASD). Domain shifts are differences in data distributions that can degrade the detection performance, and handling them is a major issue for the application of ASD systems. While currently available datasets for ASD tasks assume that occurrences of domain shifts are known, in practice, they can be difficult to detect. To handle such domain shifts, domain generalization techniques that perform well regardless of the domains should be investigated. In this paper, we present the first ASD dataset for the domain generalization techniques, called MIMII DG. The dataset consists of five machine types and three domain shift scenarios for each machine type. The dataset is dedicated to the domain generalization task with features such as multiple different values for parameters that cause domain shifts and introduction of domain shifts that can be difficult to detect, such as shifts in the background noise. Experimental results using two baseline systems indicate that the dataset reproduces domain shift scenarios and is useful for benchmarking domain generalization techniques.