DCASE 2024 Challenge Task 2 Evaluation Dataset

<Important !  Version v2 of "DCASE 2024 Challenge Task 2 Additional Training dataset" has been published on 23 May, 2024>
Version v2 of the "DCASE 2024 Challenge Task 2 Additional Training dataset", which corresponds to this "DCASE 2024 Challenge Task 2 Evaluation Dataset (Version v1)", has been published on https://zenodo.org/records/11259435 (23 May, 2024). In the "DCASE 2024 Challenge Task 2 Additional Training dataset", data files for 3DPrinter and RoboticArm are updated from Version v1 and are renamed as "eval_data_3DPrinter_train_r2.zip" and "eval_data_RoboticArm_train_r2.zip". Please use those files for the "Additional Training dataset" in DCASE 2024 Challenge Task 2. (Other files have not been changed from Version v1.) We apologize for your inconvenience.

<The data files will be made publicly accessible on June 1st, 2024>

Description

This dataset is the "evaluation dataset" for the DCASE 2024 Challenge Task 2 "First-Shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring".

The data consists of the normal/anomalous operating sounds of nine 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 10 seconds. The following nine types of real/toy machines are used in this task:

• 3DPrinter
• AirCompressor
• BrushlessMotor
• HairDryer
• HoveringDrone
• RoboticArm
• Scanner
• ToothBrush
• ToyCircuit

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 additional training dataset is one of nine: 3D-printer, air compressor, brushless motor, hair dryer, hovering drone, robotic arm, document scanner (scanner), toothbrush, and Toy circuit.
• 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 nine machine types. For each machine type, one section is provided, and the section is a complete set of test data. A set of training data corresponding to this test data is provided in another seperate zenodo page as an "additional training dataset" for the DCASE 2024 Challenge task 2 (https://zenodo.org/records/11259435). For each section, this dataset provides 200 clips of test data.

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

- /eval_data  

    - /raw
        - /3DPrinter
            - /test  
                - /section_00_0001.wav  
                - ...  
                - /section_00_0200.wav  
    - /AirCompressor (The other machine types have the same directory structure as 3DPrinter.)  
    - /BrushlessMotor
    - /HairDryer
    - /HoveringDrone
    - /RoboticArm
    - /Scanner
    - /ToothBrush
    - /ToyCircuit

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., NTT Corporation and STMicroelectronics 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, tomoya.nishida.ax@hitachi.com
• Keisuke Imoto, keisuke.imoto@ieee.org
• Noboru Harada, noboru@ieee.org
• Daisuke Niizumi, daisuke.niizumi.dt@hco.ntt.co.jp
• Yohei Kawaguchi, yohei.kawaguchi.xk@hitachi.com