DCASE 2022 Challenge Task 2 Development Dataset

Description This dataset is the "development dataset" for the DCASE 2022 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques". The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel 10-second audio that includes both a machine's operating sound and environmental noise. The following seven types of real/toy machines are used in this task: Fan Gearbox Bearing Slide rail ToyCar ToyTrain Valve Overview of the task Anomalous sound detection (ASD) is the task of identifying whether the sound emitted from a target machine is normal or anomalous. Automatic detection of mechanical failure is an essential technology in the fourth industrial revolution, which involves artificial intelligence (AI)-based factory automation. Prompt detection of machine anomalies by observing sounds is useful for monitoring the condition of machines. This task is the follow-up to DCASE 2020 Task 2 and DCASE 2021 Task 2. The task this year is to detect anomalous sounds under three main conditions: 1. Only normal sound clips are provided as training data (i.e., unsupervised learning scenario). In real-world factories, anomalies rarely occur and are highly diverse. Therefore, exhaustive patterns of anomalous sounds are impossible to create or collect and unknown anomalous sounds that were not observed in the given training data must be detected. This condition is the same as in DCASE 2020 Task 2 and DCASE 2021 Task 2. 2. Factors other than anomalies change the acoustic characteristics between training and test data (i.e., domain shift). In real-world cases, operational conditions of machines or environmental noise often differ between the training and testing phases. For example, the operation speed of a conveyor can change due to seasonal demand, or environmental noise can fluctuate depending on the states of surrounding machines. This condition is the same as in DCASE 2021 Task 2. 3. In test data, samples unaffected by domain shifts (source domain data) and those affected by domain shifts (target domain data) are mixed, and the source/target domain of each sample is not specified. Therefore, the model must detect anomalies regardless of the domain (i.e., domain generalization). Definition We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.". "Machine type" indicates the kind of machine, which in this task is one of seven: fan, gearbox, bearing, slide rail, valve, ToyCar, and ToyTrain. A section is defined as a subset of the dataset for calculating performance metrics. Each section is dedicated to a specific type of domain shift. The source domain is the domain under which most of the training data and part of the test data were recorded, and the target domain is a different set of domains under which a few of the training data and part of the test data were recorded. There are differences between the source and target domains in terms of operating speed, machine load, viscosity, heating temperature, type of environmental noise, SNR, etc. Attributes are parameters that define states of machines or types of noise. Dataset This dataset consists of three sections for each machine type (Sections 00, 01, and 02), and each section is a complete set of training and test data. For each section, this dataset provides (i) 990 clips of normal sounds in the source domain for training, (ii) ten clips of normal sounds in the target domain for training, and (iii) 100 clips each of normal and anomalous sounds for the test. The source/target domain of each sample is provided. Additionally, the attributes of each sample in the training and test data are provided in the file names and attribute csv files. File names and attribute csv files File names and attribute csv files provide reference labels for each clip. The given reference labels for each training/test clip include machine type, section index, normal/anomaly information, and attributes regarding the condition other than normal/anomaly. The machine type is given by the directory name. The section index is given by their respective file names. For the datasets other than the evaluation dataset, the normal/anomaly information and the attributes are given by their respective file names. Attribute csv files are for easy access to attributes that cause domain shifts. In these files, the file names, name of parameters that cause domain shifts (domain shift parameter, dp), and the value or type of these parameters (domain shift value, dv) are listed. Each row takes the following format: [filename (string)], [d1p (string)], [d1v (int | float | string)], [d2p], [d2v]... Recording procedure Normal/anomalous operating sounds of machines and its related equipment are recorded. Anomalous sounds were collected by deliberately damaging target machines. For simplifying the task, we use only the first channel of multi-channel recordings; all recordings are regarded as single-channel recordings of a fixed microphone. We mixed a target machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise samples were recorded in several real factory environments. We will publish papers on the dataset to explain the details of the recording procedure by the submission deadline. Directory structure - /dev_data - /fan - /train (only normal clips) - /section_00_source_train_normal_0000_<attribute>.wav - ... - /section_00_source_train_normal_0989_<attribute>.wav - /section_00_target_train_normal_0000_<attribute>.wav - ... - /section_00_target_train_normal_0009_<attribute>.wav - /section_01_source_train_normal_0000_<attribute>.wav - ... - /section_02_target_train_normal_0009_<attribute>.wav - /test - /section_00_source_test_normal_0000_<attribute>.wav - ... - /section_00_source_test_normal_0049_<attribute>.wav - /section_00_source_test_anomaly_0000_<attribute>.wav - ... - /section_00_source_test_anomaly_0049_<attribute>.wav - /section_00_target_test_normal_0000_<attribute>.wav - ... - /section_00_target_test_normal_0049_<attribute>.wav - /section_00_target_test_anomaly_0000_<attribute>.wav - ... - /section_00_target_test_anomaly_0049_<attribute>.wav - /section_01_source_test_normal_0000_<attribute>.wav - ... - /section_02_target_test_anomaly_0049_<attribute>.wav - attributes_00.csv (attribute csv for section 00) - attributes_01.csv (attribute csv for section 01) - attributes_02.csv (attribute csv for section 02) - /gearbox (The other machine types have the same directory structure as fan.) - /bearing - /slider (`slider` means "slide rail") - /ToyCar - /ToyTrain - /valve Baseline system Two baseline systems are available on the Github repository baseline_ae and baseline_mobile_net_v2. The baseline systems provide a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. They are good starting points, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task. Condition of use This dataset was created jointly by Hitachi, Ltd. and NTT Corporation and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. Citation If you use this dataset, please cite all the following three papers. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi, Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. In arXiv e-prints: 2206.05876, 2022. [URL] Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. In arXiv e-prints: 2205.13879, 2022. [URL] 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. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] Contact If there is any problem, please contact us: Kota Dohi, [e-mail] Daisuke Niizumi, [e-mail] Yohei Kawaguchi, [e-mail] Keisuke Imoto, [e-mail]### 数据集说明 本数据集为DCASE 2022挑战赛任务2「应用域泛化技术的机器状态监测无监督异常声音检测」的开发数据集（development dataset）。数据集包含7类真实/玩具机器的正常/异常运行声音。每条录音为单通道10秒音频，同时包含机器运行声与环境噪声。本任务使用的7类机器分别为：风扇（Fan）、齿轮箱（Gearbox）、轴承（Bearing）、滑轨（Slide rail）、玩具汽车（ToyCar）、玩具火车（ToyTrain）、阀门（Valve）。 ### 任务概述 异常声音检测（Anomalous Sound Detection, ASD）任务旨在识别目标机器发出的声音是否正常。机械故障自动检测是第四次工业革命中的核心技术之一，其关联基于人工智能（Artificial Intelligence, AI）的工厂自动化场景。通过声音监测实现机器异常的及时检测，对于机器状态监测具有重要实用价值。本任务是DCASE 2020任务2与DCASE 2021任务2的后续任务。今年的任务需在三大核心条件下完成异常声音检测： 1. 仅将正常声音片段作为训练数据（即无监督学习场景）。在现实工厂中，异常样本稀少且类型多样，无法穷尽收集所有异常声音模式，因此模型需检测训练集中未出现过的未知异常声音。该条件与DCASE 2020任务2、DCASE 2021任务2一致。 2. 训练与测试数据的声学特征存在除异常之外的其他变化因素（即域偏移（domain shift））。实际场景中，机器运行条件或环境噪声在训练与测试阶段往往存在差异。例如，传送带的运行速度可能随季节性需求变化，或环境噪声会随周边机器的运行状态波动。该条件与DCASE 2021任务2一致。 3. 测试数据中同时包含未受域偏移影响的样本（源域（source domain）数据）与受域偏移影响的样本（目标域（target domain）数据），且未指定每个样本的源/目标域。因此模型需实现与域无关的异常检测（即域泛化（domain generalization））。 ### 术语定义 本任务的关键术语定义如下： - **机器类型（machine type）**：指机器的类别，本任务包含7类：风扇、齿轮箱、轴承、滑轨、阀门、玩具汽车、玩具火车。 - **分段（section）**：用于计算性能指标的数据集子集，每个分段对应一类特定的域偏移场景。 - **源域（source domain）**：大部分训练数据与部分测试数据的采集域；**目标域（target domain）**：少量训练数据与部分测试数据的采集域，与源域存在差异。两类域在运行速度、机器负载、介质粘度、加热温度、环境噪声类型、信噪比（Signal-to-Noise Ratio, SNR）等参数上存在区别。 - **属性（attributes）**：用于定义机器运行状态或噪声类型的参数。 ### 数据集详情 本数据集针对每类机器类型包含3个分段（分段00、01、02），每个分段均包含完整的训练与测试数据集。针对每个分段，数据集提供： (i) 990条源域正常声音片段用于训练； (ii) 10条目标域正常声音片段用于训练； (iii) 各100条正常与异常声音片段用于测试。 每个样本的源/目标域信息均已提供。此外，训练与测试数据中每个样本的属性信息可通过文件名与属性CSV文件获取。 ### 文件名与属性CSV文件 文件名与属性CSV文件为每条音频片段提供参考标签。训练/测试片段的参考标签包含机器类型、分段索引、正常/异常标注，以及除正常/异常状态外的工况属性。 机器类型由所在目录名指定；分段索引由文件名指定。除评估数据集外，正常/异常标注与属性信息均包含在文件名中。 属性CSV文件用于简化域偏移相关属性的检索。此类文件中列出了文件名、引发域偏移的参数名称（域偏移参数（domain shift parameter, dp）），以及这些参数的取值或类型（域偏移值（domain shift value, dv））。每行格式为：`[文件名（字符串）], [d1p（字符串）], [d1v（整数|浮点数|字符串）], [d2p], [d2v]...` ### 录制流程 录制了机器及其配套设备的正常/异常运行声音。异常声音通过人为损坏目标机器采集得到。为简化任务，仅使用多通道录音的第一声道，所有录音均视为固定麦克风采集的单通道音频。将目标机器声音与环境噪声混合，仅提供带噪的训练/测试音频。环境噪声样本采集自多个真实工厂环境。本数据集的详细录制流程相关论文将在提交截止日前发布。 ### 目录结构 - /dev_data - /fan - /train（仅包含正常片段） - /section_00_source_train_normal_0000_<attribute>.wav - ... - /section_00_source_train_normal_0989_<attribute>.wav - /section_00_target_train_normal_0000_<attribute>.wav - ... - /section_00_target_train_normal_0009_<attribute>.wav - /section_01_source_train_normal_0000_<attribute>.wav - ... - /section_02_target_train_normal_0009_<attribute>.wav - /test - /section_00_source_test_normal_0000_<attribute>.wav - ... - /section_00_source_test_normal_0049_<attribute>.wav - /section_00_source_test_anomaly_0000_<attribute>.wav - ... - /section_00_source_test_anomaly_0049_<attribute>.wav - /section_00_target_test_normal_0000_<attribute>.wav - ... - /section_00_target_test_normal_0049_<attribute>.wav - /section_00_target_test_anomaly_0000_<attribute>.wav - ... - /section_00_target_test_anomaly_0049_<attribute>.wav - /section_01_source_test_normal_0000_<attribute>.wav - ... - /section_02_target_test_anomaly_0049_<attribute>.wav - attributes_00.csv（分段00的属性CSV文件） - attributes_01.csv（分段01的属性CSV文件） - attributes_02.csv（分段02的属性CSV文件） - /gearbox（其余机器类型的目录结构与fan一致） - /bearing - /slider（对应「Slide rail」滑轨） - /ToyCar - /ToyTrain - /valve ### 基线系统 GitHub仓库中提供了两个基线系统：`baseline_ae`与`baseline_mobile_net_v2`。基线系统提供了简单的入门级方案，可在本任务2的数据集上获得合理的性能表现，尤其适合希望熟悉异常声音检测任务的入门研究者作为起步参考。 ### 使用条款 本数据集由日立（Hitachi, Ltd.）与日本电信电话（NTT Corporation）联合开发，采用知识共享署名-非商业性使用-相同方式共享4.0国际许可协议（Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International, CC BY-NC-SA 4.0）发布。 ### 引用声明 若使用本数据集，请引用以下三篇论文： 1. Kota Dohi, Keisuke Imoto, Noboru Harada, Daisuke Niizumi, Yuma Koizumi, Tomoya Nishida, Harsh Purohit, Takashi Endo, Masaaki Yamamoto, Yohei Kawaguchi. Description and Discussion on DCASE 2022 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring Applying Domain Generalization Techniques. arXiv e-prints: 2206.05876, 2022. 2. Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo, Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi. MIMII DG: sound dataset for malfunctioning industrial machine investigation and inspection for domain generalization task. arXiv e-prints: 2205.13879, 2022. 3. 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. Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. ### 联系方式 如有任何问题，请联系以下人员： - Kota Dohi: [e-mail] - Daisuke Niizumi: [e-mail] - Yohei Kawaguchi: [e-mail] - Keisuke Imoto: [e-mail]

搜集汇总

背景与挑战

背景概述

DCASE 2022 Challenge Task 2开发数据集是一个用于机器状态监测的无监督异常声音检测数据集，包含7种机器的正常和异常操作声音录音，旨在测试模型在领域偏移情况下的泛化能力。