DOI: 10.5281/zenodo.4562015
title: DCASE 2021 Challenge Task 2 Development Dataset

description: <strong>Description</strong> This dataset is the "development dataset" for the <strong>DCASE 2021 Challenge Task 2 "Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions"</strong>. 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 Pump Slide rail ToyCar ToyTrain Valve <strong>Why focus on domain shift?</strong> The task setup of the 2020 version was the ASD under ideal conditions. The training- and testing-phase datasets were generated under the same recording conditions, and enough normal training clips recorded under the test domain were made available. In contrast, real-world cases are more complicated and often involve different machine operating conditions between the training and testing phases. A frequent example of this is when the motor speed continuously varies in a conveyor transporting products on a production line based on the production volume in response to product demand. Since there is infinite variation in rotation speed, the sound will also change with infinite variation. Due to the seasonal demand for many products, a limited period of recording training data limits the motor speed during that period (e.g., 200-300 rpm for autumn) and variations in the training data. However, in the test phase, the ASD system must continue to monitor the conveyor through all seasons, so it must be able to monitor all possible motor speed conditions, including those that differ from the training data (such as 100-400 rpm). In addition to the conditions of the machine, environmental noise conditions (SNR, sound characteristics, etc.) also fluctuate uncontrollably depending on the seasonal demand. In such a situation, the normal state's distribution will be changed (i.e., domain shift).<br> <strong>Definition</strong> First, we define some important terms in this task: "machine type," "section," "source domain," and "target domain." The machine type means the kind of machine, which can be one of seven in this task: fan, gearbox, pump, slide rail, ToyCar, ToyTrain, and valve. The section is defined as a subset of the dataset for calculating performance metrics and is almost identical to what was called "machine ID" in the 2020 version. In the 2020 version, there was a one-to-one correspondence between machine IDs and products, but in the 2021 version, the same product may appear in different sections. Different products may appear in the same section. The source domain means the condition under which most of the training data was recorded, and the target domain means a different condition under which some of the test data was recorded. The source and target domains differ in terms of operating speed, machine load, viscosity, heating temperature, environmental noise, SNR, etc. <strong>Data</strong> This dataset consists of three sections for each machine type (Section 00, 01, and 02), and each section is a complete set of training and test data. For each section, this dataset provides (i) around 1,000 clips of normal sounds in a source domain for training, (ii) only three clips of normal sounds in a target domain for training, (iii) around 100 clips each of normal and anomalous sounds in the source domain for the test, and (iv) around 100 clips each of normal and anomalous sounds in the target domain for the test.<br> <strong>Recording procedure</strong> Normal/anomalous operating sounds of machines and related equipment were recorded. Anomalous sounds were collected by deliberately damaging machines. To simplify the task, we only used the first channel of the multi-channel recordings; all recordings were regarded as single-channel recordings from a fixed microphone. We mixed a machine sound with environmental noise, and only noisy recordings are provided as training/test data. The environmental noise clips 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.<br> <strong>Reference labels</strong> The given labels for each training/test clip are machine type, section index, normal/anomaly information, and brief attribute information about conditions other than normal/abnormal. The machine type information 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 is given by their respective file names. For the training data, the attribute information is given by their respective file names.<br> <strong>Directory structure</strong> When you unzip the files downloaded from the GitHub repository and Zenodo, you can see the following directory structure. As described in the Dataset section, the machine type information is given by directory name, and the section index, domain, and the condition information are given by file name, as: /dev_data /fan /train (only normal clips) /section_00_source_train_normal_0000_&lt;attribute&gt;.wav ... /section_00_source_train_normal_0999_&lt;attribute&gt;.wav /section_00_target_train_normal_0000_&lt;attribute&gt;.wav /section_00_target_train_normal_0001_&lt;attribute&gt;.wav /section_00_target_train_normal_0002_&lt;attribute&gt;.wav /section_01_source_train_normal_0000_&lt;attribute&gt;.wav ... /section_02_target_train_normal_0999_&lt;attribute&gt;.wav /source_test /section_00_source_test_normal_0000.wav ... /section_00_source_test_normal_0099.wav /section_00_source_test_anomaly_0000.wav ... /section_00_source_test_anomaly_0099.wav /section_01_source_test_normal_0000.wav ... /section_02_source_test_anomaly_0099.wav /target_test /section_00_target_test_normal_0000.wav ... /section_00_target_test_normal_0099.wav /section_00_target_test_anomaly_0000.wav ... /section_00_target_test_anomaly_0099.wav /section_01_target_test_normal_0000.wav ... /section_02_target_test_anomaly_0099.wav /gearbox (The other machine types have the same directory structure as fan.) /pump /slider /ToyCar /ToyTrain /valve The paths of audio files are: "/dev_data/&lt;machine_type&gt;/train/section_[0-9]+_&lt;domain&gt;_train_normal_[0-9]+_&lt;attribute&gt;.wav" "/dev_data/&lt;machine_type&gt;/source_test/section_[0-9]+_source_test_normal_[0-9]+.wav" "/dev_data/&lt;machine_type&gt;/source_test/section_[0-9]+_source_test_anomaly_[0-9]+.wav" "/dev_data/&lt;machine_type&gt;/target_test/section_[0-9]+_target_test_normal_[0-9]+.wav" "/dev_data/&lt;machine_type&gt;/target_test/section_[0-9]+_target_test_anomaly_[0-9]+.wav" For example, the machine type, section, and domain of "/fan/train/section_01_source_train_normal_0108_strenght_1_big_ambient.wav" are "fan", "section 01", and "source", respectively, and its condition is normal. The machine type, section, and domain of "/gearbox/test/section_00_target_test_anomaly_0024.wav" are "gearbox", "section 00", and "target", respectively, and its condition is anomalous. <strong>Baseline system</strong> Two simple baseline systems are available on the Github repository [URL] and [URL]. 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. <strong>Conditions of use</strong> This dataset was created jointly by <strong>Hitachi, Ltd. </strong>and <strong>NTT Corporation</strong> and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license. <strong>Publication</strong> If you use this dataset, please cite all the following three papers: Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo, "Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions," in arXiv e-prints: 2106.04492, 2021. [URL] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito, "ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions," in arXiv e-prints: 2106.02369, 2021. [URL] Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, "MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions," in arXiv e-prints: 2105.02702, 2021. [URL] <br> <strong>Feedback</strong> If there is any problem, please contact us: Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp Keisuke Imoto, keisuke.imoto@ieee.org
description: {"references": ["Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo, \"Description and Discussion on DCASE 2021 Challenge Task 2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring under Domain Shifted Conditions,\" in arXiv e-prints: 2106.04492, 2021.", "Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, Shoichiro Saito, \"ToyADMOS2: Another Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection under Domain Shift Conditions,\" in arXiv e-prints: 2106.02369, 2021.", "Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi, \"MIMII DUE: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection with Domain Shifts due to Changes in Operational and Environmental Conditions,\" in arXiv e-prints: 2105.02702, 2021."]}