DCASE 2020 Challenge Task 2 Evaluation Dataset

Description

This dataset is the "evaluation dataset" for the DCASE 2020 Challenge Task 2 "Unsupervised Detection of Anomalous Sounds for Machine Condition Monitoring" [task description]. 

In the task, three datasets have been released: "development dataset", "additional training dataset", and "evaluation dataset". This evaluation dataset was the last of the three released. This dataset includes around 400 samples for each Machine Type and Machine ID used in the evaluation dataset, none of which have a condition label (i.e., normal or anomaly).

The recording procedure and data format are the same as the development dataset and additional training dataset. The Machine IDs in this dataset are the same as those in the additional training dataset. For more information, please see the pages of the development dataset and the task description. 

After the DCASE 2020 Challenge, we released the ground truth for this evaluation dataset.

Directory structure

Once you unzip the downloaded files from Zenodo, you can see the following directory structure. Machine Type information is given by directory name, and Machine ID and condition information are given by file name, as:

/eval_data

• /ToyCar
  • /test  (Normal and anomaly data for all Machine IDs are included, but they do not have a condition label.)
    • /id_05_00000000.wav
    • ...
    • /id_05_00000514.wav
    • /id_06_00000000.wav
    • ...
    • /id_07_00000514.wav
• /ToyConveyor (The other Machine Types have the same directory structure as ToyCar.)
• /fan
• /pump
• /slider
• /valve

The paths of audio files are:

• "/eval_data/<Machine_Type>/test/id_<Machine_ID>_[0-9]+.wav"

For example, the Machine Type and Machine ID of "/ToyCar/test/id_05_00000000.wav" are "ToyCar" and "05", respectively. Unlike the development dataset and additional training dataset, its condition label is hidden. 

Baseline system

A simple baseline system is available on the Github repository [URL]. The baseline system provides a simple entry-level approach that gives a reasonable performance in the dataset of Task 2. It is a good starting point, especially for entry-level researchers who want to get familiar with the anomalous-sound-detection task.

Conditions of use

This dataset was created jointly by NTT Corporation and Hitachi, Ltd. and is available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) license.

Publication

If you use this dataset, please cite all the following three papers:

Yuma Koizumi, Shoichiro Saito, Noboru Harada, Hisashi Uematsu, and Keisuke Imoto, "ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection," in Proc. of IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2019. [pdf]

Harsh Purohit, Ryo Tanabe, Kenji Ichige, Takashi Endo, Yuki Nikaido, Kaori Suefusa, and Yohei Kawaguchi, “MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection,” in Proc. 4th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2019. [pdf]

Yuma Koizumi, Yohei Kawaguchi, Keisuke Imoto, Toshiki Nakamura, Yuki Nikaido, Ryo Tanabe, Harsh Purohit, Kaori Suefusa, Takashi Endo, Masahiro Yasuda, and Noboru Harada, "Description and Discussion on DCASE2020 Challenge Task2: Unsupervised Anomalous Sound Detection for Machine Condition Monitoring," in Proc. 5th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE), 2020. [pdf]

Feedback

If there is any problem, please contact us:

• Yuma Koizumi, koizumi.yuma@ieee.org
• Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com
• Keisuke Imoto, keisuke.imoto@ieee.org