DCASE 2026 Challenge Task 2 Development Dataset 核心 · 已核验

doi:10.5281/zenodo.19336329

<Important !  Notes on updating data file for ToyCar (8 April, 2026)>Due to some data issues, the data file for ToyCar have been updated. The old version of this file is deleted and the new version is renamed as "dev_ToyCar_r2.zip". Please use this file for the DCASE 2026 Challenge Task 2. (Other files have not been changed)

Description

This dataset is the "development dataset" for the DCASE 2026 Challenge Task 2 "Noise-aware 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 10-sec or 12-sec 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

ToyCarEmu (Emu)

Fan

Gearbox (Emu)

Bearing (Emu)

Slide rail (Emu)

Valve (Emu)

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 2025 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, which is called UASD (unsupervised ASD). This is the same requirement as in the previous tasks.2. Detect anomalies regardless of domain shifts (domain generalizati

落地页

https://zenodo.org/doi/10.5281/zenodo.19336329

许可证

CC-BY-NC-SA-4.0 (判读置信:unknown)

国内可访问性

国内直连:直连超时(慢或不稳定,非封锁证据) (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。

发布年份

2026

发布方

Zenodo

设备类型

gearbox rolling_bearing

PHM 任务

anomaly_detection condition_monitoring domain_adaptation