mlsanityData to 7 Oct 2026

ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions

arXiv 2106.02369 ↗First posted 4 Jun 2021eess.AScited by 26

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Abstract (arXiv)

This paper proposes a new large-scale dataset called "ToyADMOS2" for anomaly detection in machine operating sounds (ADMOS). As did for our previous ToyADMOS dataset, we collected a large number of operating sounds of miniature machines (toys) under normal and anomaly conditions by deliberately damaging them but extended with providing controlled depth of damages in anomaly samples. Since typical application scenarios of ADMOS often require robust performance under domain-shift conditions, the ToyADMOS2 dataset is designed for evaluating systems under such conditions. The released dataset consists of two sub-datasets for machine-condition inspection: fault diagnosis of machines with geometrically fixed tasks and fault diagnosis of machines with moving tasks. Domain shifts are represented by introducing several differences in operating conditions, such as the use of the same machine type but with different machine models and parts configurations, different operating speeds, microphone arrangements, etc. Each sub-dataset contains over 27 k samples of normal machine-operating sounds and over 8 k samples of anomalous sounds recorded with five to eight microphones. The dataset is freely available for download at https://github.com/nttcslab/ToyADMOS2-dataset and https://doi.org/10.5281/zenodo.4580270.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jun 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 5; 2025: 5; 2026: 7.

26 papers.

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  1. 2022MIMII DG: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection for Domain Generalization Taskcited by 23
  2. 2022Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniquescited by 18
  3. 2023Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 18
  4. 2021Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditionscited by 16
  5. 2024Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoringcited by 11
  6. 2023Unsupervised Anomaly Detection and Localization of Machine Audio: A GAN-based Approachcited by 9
  7. 2023Self-Supervised Learning for Anomalous Sound Detectioncited by 8
  8. 2023Why do Angular Margin Losses work well for Semi-Supervised Anomalous Sound Detection?cited by 4
  9. 2024Deep Generic Representations for Domain-Generalized Anomalous Sound Detectioncited by 4
  10. 2024Improving Anomalous Sound Detection via Low-Rank Adaptation Fine-Tuning of Pre-Trained Audio Modelscited by 3
  11. 2024ASD-Diffusion: Anomalous Sound Detection with Diffusion Modelscited by 3
  12. 2021Multimedia Datasets for Anomaly Detection: A Reviewcited by 2
14 more
  1. 2022Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous Sound Detectioncited by 2
  2. 2025FISHER: A Foundation Model for Multi-Modal Industrial Signal Comprehensive Representationcited by 2
  3. 2025An Enhanced Audio Feature Tailored for Anomalous Sound Detection Based on Pre-trained Modelscited by 1
  4. 2026False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Controlcited by 1
  5. 2026Can We Predict Anomaly Detection Performance from Embedding-Space Geometry?cited by 1
  6. 2024Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detectioncited by 0
  7. 2025MIMII-Agent: Leveraging LLMs with Function Calling for Relative Evaluation of Anomalous Sound Detectioncited by 0
  8. 2025Improving Anomalous Sound Detection with Attribute-aware Representation from Domain-adaptive Pre-trainingcited by 0
  9. 2025Contrastive Learning with Spectrum Information Augmentation in Abnormal Sound Detectioncited by 0
  10. 2026A Framework for Evaluating Faithfulness in Explainable AI for Machine Anomalous Sound Detection Using Frequency-Band Perturbationcited by 0
  11. 2026Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detectioncited by 0
  12. 2026Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detectioncited by 0
  13. 2026Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Datacited by 0
  14. 2026Anomaly-Free Self-Optimization via AUC Boundscited by 0