We present the task description of the Detection and Classification of
Acoustic Scenes and Events (DCASE) 2024 Challenge Task 2: First-shot
unsupervised anomalous sound detection (ASD) for machine condition monitoring.
Continuing from last year's DCASE 2023 Challenge Task 2, we organize the task
as a first-shot problem under domain generalization required settings. The main
goal of the first-shot problem is to enable rapid deployment of ASD systems for
new kinds of machines without the need for machine-specific hyperparameter
tunings. This problem setting was realized by (1) giving only one section for
each machine type and (2) having completely different machine types for the
development and evaluation datasets. For the DCASE 2024 Challenge Task 2, data
of completely new machine types were newly collected and provided as the
evaluation dataset. In addition, attribute information such as the machine
operation conditions were concealed for several machine types to mimic
situations where such information are unavailable. We will add challenge
results and analysis of the submissions after the challenge submission
deadline.

2024 年 DCASE 挑战任务 2 的任务描述：机器条件监控中首次无监督异常声音检测（ASD），以领域泛化为特点的一次性问题，旨在实现对新型机器的快速部署，无需机器特定的超参数调整。

DCASE 2024 挑战任务 2 的描述和讨论：面向机器状态监测的一次性无监督异常声音检测

Description and Discussion on DCASE 2024 Challenge Task 2: First-Shot  Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

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.

本文介绍了 DCASE 2023 Challenge Task 2 的任务描述：“适用于机器状态监测的无监督异声检测” 以及采用了仅有一些正常样本的新机器的第一次尝试该问题的解决方案。

DCASE 2023 挑战赛任务 2 的描述与讨论：针对机器状态监测的一次性无监督异常声音检测

Description and Discussion on DCASE 2023 Challenge Task 2: First-Shot  Unsupervised Anomalous Sound Detection for Machine Condition Monitoring

In this paper, we present the task description and discuss the results of the
DCASE 2020 Challenge Task 2: Unsupervised Detection of Anomalous Sounds for
Machine Condition Monitoring. The goal of anomalous sound detection (ASD) is to
identify whether the sound emitted from a target machine is normal or
anomalous. The main challenge of this task is to detect unknown anomalous
sounds under the condition that only normal sound samples have been provided as
training data. We have designed this challenge as the first benchmark of ASD
research, which includes a large-scale dataset, evaluation metrics, and a
simple baseline system. We received 117 submissions from 40 teams, and several
novel approaches have been developed as a result of this challenge. On the
basis of the analysis of the evaluation results, we discuss two new approaches
and their problems.

本文介绍了 DCASE 2020 Challenge 任务 2 的任务描述和结果，并讨论了无监督检测机器异常声音方面的研究。通过介绍了大规模数据集、评价指标和基线系统来设计此挑战赛，共收到了 40 个团队的 117 份提交，基于该挑战的结果分析，本文进一步讨论和分析了两种新方法和存在的问题。