DCASE 2024 Challenge Task 2 Development Dataset

<Data files will be made accessible on April 1st, 2024.> Description This dataset is the "development dataset" for the DCASE 2024 Challenge Task 2. 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: ToyCar ToyTrain Fan Gearbox Bearing Slide rail 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-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 2023 Task 2. The task this year is to develop an ASD system that meets the following five 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 and DCASE 2023 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. This requirement is the same as in DCASE 2023 Task 2. 4. **Train a model using a limited number of machines from its machine type** While sounds from multiple machines of the same machine type can be used to enhance the detection performance, it is often the case that only a limited number of machines are available for a machine type. In such a case, the system should be able to train models using a few machines from a machine type. This requirement is the same as in DCASE 2023 Task 2. 5 . **Train a model both with or without attribute information** While additional attribute information can help enhance the detection performance, we cannot always obtain such information. Therefore, the system must work well both when attribute information is available and when it is not. The last requirement is newly introduced in DCASE 2024 Task2. 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. For several machine types, the attributes are hidden. 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, 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. Note that for machine types that has its attribute information hidden, the attribute information in each file names are only labeled as "noAttributes". 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]... For machine types that have their attribute information hidden, all columns except the filename column are left blank for each row. 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 - /fan - /train (only normal clips) - /section_00_source_train_normal_0001_<attribute>.wav - ... - /section_00_source_train_normal_0990_<attribute>.wav - /section_00_target_train_normal_0001_<attribute>.wav - ... - /section_00_target_train_normal_0010_<attribute>.wav - /test - /section_00_source_test_normal_0001_<attribute>.wav - ... - /section_00_source_test_normal_0050_<attribute>.wav - /section_00_source_test_anomaly_0001_<attribute>.wav - ... - /section_00_source_test_anomaly_0050_<attribute>.wav - /section_00_target_test_normal_0001_<attribute>.wav - ... - /section_00_target_test_normal_0050_<attribute>.wav - /section_00_target_test_anomaly_0001_<attribute>.wav - ... - /section_00_target_test_anomaly_0050_<attribute>.wav - attributes_00.csv (attribute csv for section 00) - /gearbox (The other machine types have the same directory structure as fan.) - /bearing - /slider (`slider` means "slide rail") - /ToyCar - /ToyTrain - /valve Baseline system The baseline system is available on the Github repository <https://github.com/nttcslab/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 <TBD> Contact If there is any problem, please contact us: Tomoya Nishida, [e-mail] Keisuke Imoto, [e-mail] Noboru Harada, [e-mail] Daisuke Niizumi, [e-mail] Yohei Kawaguchi, [e-mail]数据集文件将于2024年4月1日开放获取。 ## 描述 本数据集为DCASE 2024挑战赛任务2的“开发数据集”。数据集包含7类实体/玩具机器的正常与异常运行音频。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类实体/玩具机器分别为：玩具汽车（ToyCar）、玩具火车（ToyTrain）、风扇（Fan）、齿轮箱（Gearbox）、轴承（Bearing）、滑轨（Slide rail）、阀门（Valve）。 ## 任务概述 异常声音检测（Anomalous sound detection, ASD）是识别目标机器发出的声音是否正常的任务。机械故障自动检测是第四次工业革命中基于人工智能的工厂自动化的核心技术。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应用价值。本任务是DCASE 2020任务2到DCASE 2023任务2的延续。今年的任务目标是开发满足以下五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）** 由于真实工厂中异常情况罕见且类型多样，难以收集全覆盖的异常声音样本，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）** 真实场景中，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域偏移，该要求与DCASE 2022任务2、DCASE 2023任务2一致。 3. **针对全新机器类型训练模型** 针对全新机器类型，无法对训练好的模型的超参数进行调优。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。该要求与DCASE 2023任务2一致。 4. **使用同一机器类型下有限数量的机器样本训练模型** 尽管可使用同类型多台机器的音频提升检测性能，但实际场景中同类型机器往往仅能获取有限数量的样本。此时系统需能够通过同类型下少量机器样本完成模型训练。该要求与DCASE 2023任务2一致。 5. **支持带/不带属性信息的模型训练** 额外的属性信息可提升检测性能，但实际场景中无法保证总能获取此类信息。因此系统需在属性信息可用与不可用两种场景下均能表现良好。该要求为DCASE 2024任务2新增内容。 ## 术语定义 首先定义本任务中的关键术语：“机器类型”、“场景分区（section）”、“源域（source domain）”、“目标域（target domain）”与“属性（attributes）”。 - 机器类型：指机器的类别，本开发数据集包含7类：风扇、齿轮箱、轴承、滑轨、阀门、玩具汽车、玩具火车。 - 场景分区：指用于计算性能指标的数据集子集，每类机器类型对应一个场景分区。 - 源域：多数训练数据与部分测试数据的录制域；目标域：另一组域，对应剩余部分训练与测试数据的录制域。源域与目标域在运行速度、机器负载、润滑油黏度、加热温度、环境噪声类型、信噪比等方面存在差异。 - 属性：定义机器状态或噪声类型的参数。部分机器类型的属性信息未公开。 ## 数据集详情 本数据集包含7类机器类型。每类机器类型对应一个场景分区，该分区包含完整的训练与测试数据集。每个场景分区提供以下数据： (i) 源域下990条正常声音训练样本； (ii) 目标域下10条正常声音训练样本； (iii) 测试集各包含100条正常与异常声音样本。 数据集会提供每个样本对应的源/目标域信息。此外，训练与测试样本的属性信息会通过文件名与属性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、场景分区索引、正常/异常标记，以及除正常/异常状态外的其他状态属性。 机器类型由所在目录名称标识，场景分区索引由文件名标识。除评测数据集外，其余数据集的正常/异常标记与属性信息均包含在文件名中。需注意，对于属性信息未公开的机器类型，其文件名中的属性信息仅标记为“noAttributes”。 属性CSV文件用于便捷访问引发域偏移的属性信息。此类文件中会列出文件名、引发域偏移的参数（域偏移参数，domain shift parameter, dp）及其对应的值/类型（域偏移值，domain shift value, dv）。每行格式如下： [文件名（字符串）], [d1p（字符串）], [d1v（整数|浮点数|字符串）], [d2p], [d2v]... 对于属性信息未公开的机器类型，除文件名列外，其余列均留空。 ## 录制流程 录制机器及其相关设备的正常与异常运行声音。异常声音通过人为损坏目标机器采集。为简化任务，本数据集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声道音频。将目标机器音频与环境噪声混合，仅提供带噪的训练与测试样本。环境噪声样本采集自多个真实工厂环境。本数据集的录制细节将在投稿截止日期前以论文形式公开。 ## 目录结构 - /dev_data - /raw - /fan - /train（仅包含正常样本） - /section_00_source_train_normal_0001_<attribute>.wav - ... - /section_00_source_train_normal_0990_<attribute>.wav - /section_00_target_train_normal_0001_<attribute>.wav - ... - /section_00_target_train_normal_0010_<attribute>.wav - /test - /section_00_source_test_normal_0001_<attribute>.wav - ... - /section_00_source_test_normal_0050_<attribute>.wav - /section_00_source_test_anomaly_0001_<attribute>.wav - ... - /section_00_source_test_anomaly_0050_<attribute>.wav - /section_00_target_test_normal_0001_<attribute>.wav - ... - /section_00_target_test_normal_0050_<attribute>.wav - /section_00_target_test_anomaly_0001_<attribute>.wav - ... - /section_00_target_test_anomaly_0050_<attribute>.wav - attributes_00.csv（场景分区00的属性CSV文件） - /gearbox（其余机器类型目录结构与fan一致） - /bearing - /slider（此处“slider”对应“slide rail”，即滑轨） - /ToyCar - /ToyTrain - /valve ## 基线系统 基线系统可在Github仓库<https://github.com/nttcslab/dcase2023_task2_baseline_ae>获取。基线系统提供了简单的入门级方案，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开发起点。 ## 使用条款 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（Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International, CC BY-NC-SA 4.0）发布。 ## 引用信息 引用信息待补充（<TBD>）。 ## 联系方式 如有任何问题，请联系以下人员： - Tomoya Nishida：[e-mail] - Keisuke Imoto：[e-mail] - Noboru Harada：[e-mail] - Daisuke Niizumi：[e-mail] - Yohei Kawaguchi：[e-mail]

搜集汇总

背景与挑战

背景概述

DCASE 2024 Challenge Task 2开发数据集包含7种机器类型的正常/异常操作声音，用于无监督学习场景下的异常声音检测研究。数据集特点是包含单通道10秒音频(混合机器声和环境噪声)，并特别设计用于处理域偏移问题，支持模型在仅使用正常声音训练的情况下检测未知异常。