ToyADMOS2: miniature-machine operating sounds for anomalous sound detection 核心 · 已核验
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ToyADMOS2 dataset is a large-scale dataset for anomaly detection in machine operating sounds (ADMOS), designed for evaluating systems under domain-shift conditions. It consists of two sub-datasets for machine-condition inspection: fault diagnosis of machines with geometrically fixed tasks ("toy car") and fault diagnosis of machines with moving tasks ("toy train"). 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 part 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 at a 48-kHz sampling rate. A subset of the ToyADMOS2 dataset was used in the DCASE 2021 challenge task 2: Unsupervised anomalous sound detection for machine condition monitoring under domain shifted conditions. What makes this dataset different from others is that it is not used as is, but in conjunction with the tool provided on GitHub. The mixer tool lets you create datasets with any combination of recordings by describing the amount you need in a <em>recipe</em> file. The samples are compressed as MPEG-4 ALS (MPEG-4 Audio Lossless Coding) with a suffix of '.mp4' that you can load by using the <em>audioread</em> or <em>librosa</em> python module. The total size of files under a folder ToyADMOS2 is 149 GB, and the total size of example benchmark datasets that are created from the ToyADMOS2 dataset is 13.2 GB. The detail of the dataset is described in [1] and GitHub: https://github.com/nttcslab/ToyADMOS2-dataset License: see LICENSE.pdf for the detail of the license. [1] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito, "ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions," 2021. https://arxiv.org/abs/2106.02369
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- 落地页
- https://zenodo.org/records/4580270
- 许可证
- other-nc (置信:verified_official)
- 国内可访问性
-
国内直连:可达 (2026-07-11 检测)
非直连:可达 (2026-07-11 检测)
「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。 - 发布年份
- 2021
- 发布方
- Zenodo
- 别名
- ToyADMOS2
- 设备类型
other- PHM 任务
anomaly_detectiondomain_adaptation
故障工况
| description: 微型机(玩具车/玩具列车)人工损伤部件运行异常声fault_type: otherinduction: artificially_seeded |
传感器
| sensor_type: microphoneobserved_property: acoustic_pressuremounting_note: 多传声器近/远场同步录音 |
运行工况
| description: 域偏移设计:背景噪声/传声器配置/运行速度等条件系统性变化condition_type: environmentis_varying: True |
关联论文(91 篇)
仅表示这篇论文与本数据集在文献网络中相邻, 不代表该论文确实使用了本数据集。
- ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions 2021 · 首发该数据集
- ToyADMOS2 dataset: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions 2021 · 首发该数据集
- ToyADMOS2 dataset: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions 2021 · 首发该数据集
- Dynamic Training Strategies for Domain Generalization in Self-Supervised Anomaly Sound Detection 2026 · 候选引用(未核验)
- What Is That Noise: Survey of Anomalous Sound Detection Using Edge Systems 2026 · 候选引用(未核验)
- Quantitative Analysis of Proxy Tasks for Anomalous Sound Detection 2026 · 候选引用(未核验)
- How Much Does Machine Identity Matter in Anomalous Sound Detection at Test Time? 2026 · 候选引用(未核验)
- Mind the Gap: Detecting Cluster Exits for Robust Local Density-Based Score Normalization in Anomalous Sound Detection 2026 · 候选引用(未核验)
- Temporal Pooling Strategies for Training-Free Anomalous Sound Detection with Self-Supervised Audio Embeddings 2026 · 候选引用(未核验)
- Sub-Band Spectral Matching with Localized Score Aggregation for Robust Anomalous Sound Detection 2026 · 候选引用(未核验)
- 実環境下におけるデータの取得制約を考慮した異常音検知 2026 · 候选引用(未核验)
- False Sense of Safety in Selective Signal Classification: Auditing Bound Tightness and Exchangeability for Risk Control 2026 · 候选引用(未核验)
- Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detection 2026 · 候选引用(未核验)
- ECHOv2: Two-Level Band-Splitting Representation Learning for Anomalous Sound Detection 2026 · 候选引用(未核验)
- Pseudo-label distillation for discriminative anomalous sound detection 2026 · 候选引用(未核验)
- NABEATs: Noise-Aware Audio Representation Learning 2026 · 候选引用(未核验)
- Anomalous Sound Detection Meets Noise-Aware Self-Supervised Learning 2026 · 候选引用(未核验)
- Fault Identification for Industrial Machinery Using Audio Fingerprint 2025 · 用于方法验证
- Visual Feature Domain Audio Coding for Anomaly Sound Detection Application 2025 · 用于方法验证
- Local Density-Based Anomaly Score Normalization for Domain Generalization 2025 · 候选引用(未核验)
仅列前 20 篇(首发/综述优先,按年份倒序);全量见 API。
溯源(8 条)
| 日期: 2026-07-08 |
| 来源链接: https://api.datacite.org/dois/10.5281/zenodo.4580270 日期: 2026-07-08 |
| 日期: 2026-07-08 |
| 来源链接: https://zenodo.org/api/records/4580270 日期: 2026-07-08 |
| 日期: 2026-07-11 |
| 日期: 2026-07-11 |
| 日期: 2026-07-21 |
| 日期: 2026-07-25 |