We present the task description of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2023 Challenge Task 2: "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". The main goal is to enable rapid deployment of ASD systems for new kinds of machines using only a few normal samples, without the need for hyperparameter tuning. In the past ASD tasks, developed methods tuned hyperparameters for each machine type, as the development and evaluation datasets had the same machine types. However, collecting normal and anomalous data as the development dataset can be infeasible in practice. In 2023 Task 2, we focus on solving first-shot problem, which is the challenge of training a model on a few machines of a completely novel machine type. Specifically, (i) each machine type has only one section, and (ii) machine types in the development and evaluation datasets are completely different. We will add challenge results and analysis of the submissions after the challenge submission deadline.

翻译：本文介绍了检测与分类声学场景与事件（DCASE）2023挑战赛任务二：“面向机器状态监测的首次无监督异常声音检测（ASD）”。核心目标是利用少量正常样本实现新型机器ASD系统的快速部署，无需进行超参数调优。在以往的ASD任务中，由于开发集与评估集包含相同机器类型，所开发的方法需针对每种机器类型调整超参数。然而在实践中，收集开发集所需的正常与异常数据往往难以实现。2023年任务二聚焦于解决“首次问题”——即针对完全新型机器类型的少量机器进行模型训练这一挑战。具体而言：（i）每种机器类型仅包含一个区段；（ii）开发集与评估集中的机器类型完全不同。我们将在挑战赛提交截止后补充赛事结果与提交方案分析。