mlsanityData to 7 Oct 2026

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

arXiv 2105.02702 ↗First posted 6 May 2021cs.SD cs.LG eess.AScited by 9

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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

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 14 of its 24 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

14 of 24 references.

References · 14

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2 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 4; 2023: 0; 2024: 0; 2025: 3.

9 papers.

  1. 2022MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Taskcited by 23
  2. 2021Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditionscited by 16
  3. 2025SciTS: Scientific Time Series Understanding and Generation with LLMscited by 13
  4. 2021Multimedia Datasets for Anomaly Detection: A Reviewcited by 2
  5. 2022Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detectioncited by 2
  6. 2022Anomalous Sound Detection Based on Machine Activity Detectioncited by 2
  7. 2025FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representationcited by 2
  8. 2022Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motorscited by 0
  9. 2025MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detectioncited by 0