DCASE 2023 Challenge Task 2 Additional Training Dataset

Description This dataset is the "additional training dataset" for the DCASE 2023 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring". The data consists of the normal/anomalous operating sounds of seven types of real/toy machines. Each recording is a single-channel audio that includes both a machine's operating sound and environmental noise. The duration of recordings varies from 6 to 18 sec, depending on the machine type. The following seven types of real/toy machines are used: Vacuum ToyTank ToyNscale ToyDrone bandsaw grinder shaker 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-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 from DCASE 2020 Task 2 to DCASE 2022 Task 2. The task this year is to develop an ASD system that meets the following four requirements. 1. Train a model using only normal sound (unsupervised learning scenario) Because anomalies rarely occur and are highly diverse in real-world factories, it can be difficult to collect exhaustive patterns of anomalous sounds. Therefore, the system must detect unknown types of anomalous sounds that are not provided in the training data. This is the same requirement as in the previous tasks. 2. Detect anomalies regardless of domain shifts (domain generalization task) In real-world cases, the operational states of a machine or the environmental noise can change to cause domain shifts. Domain-generalization techniques can be useful for handling domain shifts that occur frequently or are hard-to-notice. In this task, the system is required to use domain-generalization techniques for handling these domain shifts. This requirement is the same as in DCASE 2022 Task 2. 3. Train a model for a completely new machine type For a completely new machine type, hyperparameters of the trained model cannot be tuned. Therefore, the system should have the ability to train models without additional hyperparameter tuning. 4. Train a model using only one machine from its machine type While sounds from multiple machines of the same machine type can be used to enhance detection performance, it is often the case that sound data from only one machine are available for a machine type. In such a case, the system should be able to train models using only one machine from a machine type. The last two requirements are newly introduced in DCASE 2023 Task2 as the "first-shot problem". Definition We first define key terms in this task: "machine type," "section," "source domain," "target domain," and "attributes.". "Machine type" indicates the type of machine, which in the development dataset 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. The source domain is the domain under which most of the training data and some of the test data were recorded, and the target domain is a different set of domains under which some of the training data and some 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, signal-to-noise ratio, etc. Attributes are parameters that define states of machines or types of noise. Dataset This dataset consists of seven machine types. For each machine type, one section is provided, and the 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. 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 - /raw - /Vacuum - /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 - /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 - attributes_00.csv (attribute csv for section 00) - /ToyTank (The other machine types have the same directory structure as Vacuum.) - /ToyNscale - /ToyDrone - /bandsaw - /grinder - /shaker Baseline system The baseline system is available on the Github repository dcase2023_task2_baseline_ae.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 papers. We will publish a paper on the description of the DCASE 2023 Task 2, so pleasure make sure to cite the paper, too. Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455, 2023. [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 Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 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] Keisuke Imoto, [e-mail] Noboru Harada, [e-mail] Daisuke Niizumi, [e-mail] Yohei Kawaguchi, [e-mail]## 数据集描述 本数据集为检测与分类声学场景与事件挑战赛(Detection and Classification of Acoustic Scenes and Events, DCASE) 2023任务2「面向机器状态监测的首样本无监督异常声音检测」的附加训练数据集。 数据集包含7类真实/玩具机器的正常与异常运行声音：每段录音均为单声道音频，同时包含机器运行声与环境噪声，时长依机器类型不同，介于6至18秒之间。本次使用的7类机器分别为：真空机(Vacuum)、玩具坦克(ToyTank)、玩具缩放模型(ToyNscale)、玩具无人机(ToyDrone)、带锯(bandsaw)、研磨机(grinder)、振动台(shaker)。 ### 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属于正常还是异常。机械故障自动检测是第四次工业革命的核心技术之一，涉及基于人工智能的工厂自动化方案。通过声音监测实现机器异常的及时检测，对机器状态监测具有重要实用价值。本任务是DCASE 2020任务2至DCASE 2022任务2的延续。本年度任务要求开发满足以下四项要求的异常声音检测系统： 1. 仅使用正常声音训练模型（无监督学习场景）：现实工厂中异常样本稀少且类型多样，难以收集全量异常声音模式，因此系统需能够检测训练集中未出现的未知类型异常声音，该要求与往届任务一致。 2. 无视域偏移检测异常（域泛化任务）：现实场景中，机器运行状态或环境噪声的变化会引发域偏移，域泛化技术可有效处理频繁出现或难以察觉的域偏移问题。本任务要求系统采用域泛化技术应对此类偏移，该要求与DCASE 2022任务2一致。 3. 为全新机器类型训练模型：对于全新机器类型，无法调整已训练模型的超参数，因此系统需具备无需额外超参数调优即可训练模型的能力。 4. 仅使用单台同类型机器训练模型：尽管同类型多台机器的声音可提升检测性能，但实际场景中常仅能获取单台机器的声音数据。此时系统需能够仅基于单台机器的样本训练模型。 上述最后两项要求为DCASE 2023任务2新增的「首样本问题」。 ### 术语定义 本任务首先定义关键术语：「机器类型」「分段」「源域」「目标域」及「属性」。 - 「机器类型」指机器的类别，开发数据集包含以下7类：风扇(fan)、齿轮箱(gearbox)、轴承(bearing)、滑轨(slide rail)、阀门(valve)、玩具车(ToyCar)及玩具火车(ToyTrain)。 - 「分段」指用于计算性能指标的数据集子集。 - 「源域」为大多数训练数据及部分测试数据的录制场景，「目标域」为另一组包含部分训练与测试数据的场景。源域与目标域在运行速度、机器负载、粘度、加热温度、环境噪声类型、信噪比等方面存在差异。 - 「属性」为定义机器状态或噪声类型的参数。 ### 数据集概况 本数据集包含7类机器。每类机器对应一个分段，该分段包含完整的训练与测试数据。针对每个分段，数据集提供：(i) 源域内990条正常声音样本用于训练；(ii) 目标域内10条正常声音样本用于训练。每个样本的源/目标域信息已提供。此外，训练与测试数据中每个样本的属性信息已通过文件名及属性CSV文件提供。 ### 文件名与属性CSV文件 文件名与属性CSV文件为每条音频样本提供参考标签。每条训练/测试样本的参考标签包含机器类型、分段索引、正常/异常信息及除正常/异常外的状态属性。机器类型由目录名指定，分段索引由各自的文件名指定。除评估数据集外，正常/异常信息及属性均通过文件名给出。 属性CSV文件用于便捷访问引发域偏移的属性信息，文件中列出了文件名、引发域偏移的参数名称（域偏移参数dp）及这些参数的取值或类型（域偏移值dv）。每行格式如下：[文件名（字符串）]、[d1p（字符串）]、[d1v（整型|浮点型|字符串）]、[d2p]、[d2v]…… ### 录制流程 录制机器及其配套设备的正常与异常运行声音。异常声音通过故意损坏目标机器获取。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录制的单声道音频。将目标机器声音与环境噪声混合，仅提供带噪的训练/测试样本。环境噪声样本录制自多个真实工厂环境。我们将在提交截止日前发布关于本数据集录制流程细节的论文。 ### 目录结构 - /dev_data - /raw - /Vacuum - /train（仅包含正常样本） - 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 - /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 - attributes_00.csv（分段00的属性CSV文件） - /ToyTank（其余机器类型目录结构与Vacuum一致） - /ToyNscale - /ToyDrone - /bandsaw - /grinder - /shaker ### 基线系统 基线系统可在Github仓库`dcase2023_task2_baseline_ae`中获取。基线系统提供了一种简单的入门级方案，在本任务2的数据集上可取得合理的性能表现，尤其适合希望熟悉异常声音检测任务的入门研究者作为研究起点。 ### 使用许可 本数据集由日立有限公司(Hitachi, Ltd.)与日本电信电话株式会社(NTT Corporation)联合制作，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(CC BY-NC-SA 4.0)发布。 ### 引用规范 若使用本数据集，请引用以下全部论文。我们将发布关于DCASE 2023任务2描述的论文，请务必一并引用。 1. Noboru Harada, Daisuke Niizumi, Yasunori Ohishi, Daiki Takeuchi, and Masahiro Yasuda. First-shot anomaly detection for machine condition monitoring: A domain generalization baseline. In arXiv e-prints: 2303.00455, 2023. [URL] 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. In Proceedings of the 7th Detection and Classification of Acoustic Scenes and Events 2022 Workshop (DCASE2022), 31-35. Nancy, France, November 2022. [URL] 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. In Proceedings of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 1–5. Barcelona, Spain, November 2021. [URL] ### 联系方式 如有任何问题，请联系以下人员： - Kota Dohi, [e-mail] - Keisuke Imoto, [e-mail] - Noboru Harada, [e-mail] - Daisuke Niizumi, [e-mail] - Yohei Kawaguchi, [e-mail]