This paper presents an overview of DCASE 2026 Challenge Task 2, titled "Noise-aware unsupervised anomalous sound detection (UASD) for machine condition monitoring." The task aims to advance noise-robust anomalous sound detection for machine condition monitoring under the unsupervised setting, where only normal machine sounds are available for training. Reliable detection under noisy conditions is crucial for practical deployment, but previous DCASE Task 2 settings provided limited information about environmental noise, potentially limiting UASD performance in highly noisy situations. To address this limitation, DCASE 2026 allows participants to exploit two-channel audio samples simultaneously captured at locations near and far from the target machine. Since the distant microphone is expected to contain relatively stronger environmental noise and weaker direct machine sounds, it may help distinguish environmental noise components from the target machine sounds. After the challenge submission deadline, challenge results and an analysis of the submitted systems will be added.

翻译：本文概述了DCASE 2026挑战赛任务二“面向机器状态监测的噪声感知无监督异常声音检测”。该任务旨在推进无监督场景下（即仅可利用正常机器声音进行训练）机器状态监测中具有噪声鲁棒性的异常声音检测技术。在实际部署中，噪声环境下的可靠检测至关重要，但以往DCASE任务二的设置提供的环境噪声信息有限，可能限制高噪声场景下的UASD性能。为克服这一局限，DCASE 2026允许参与者利用在目标机器远近不同位置同步采集的双通道音频样本。由于远距离麦克风预计会捕获相对更强的环境噪声和更弱的直接机器声音，因而可能有助于区分环境噪声成分与目标机器声音。挑战赛提交截止后，将补充提交系统的结果与分析。