零样本无监督检测设备异常声音，快速部署新机型监测系统
Description and Discussion on DCASE 2025 Challenge Task 2: First-shot Unsupervised Anomalous Sound Detection for Machine Condition Monitoring
- 基于领域泛化框架设计零样本异常声音检测任务
- 119份提交显示预训练模型微调与冻结模型均表现良好
- 适合需快速适配新设备的工业场景应用
本文介绍了DCASE 2025挑战赛任务2的任务说明，主题为“面向机器状态监测的首例无监督异常声音检测（ASD）”。该任务在DCASE 2024任务2基础上，采用领域泛化框架下的首例学习范式，旨在实现无需针对特定机器调整超参数即可快速部署异常声音检测系统。2025年任务评估数据集包含此前未见过的机器类型声音。共收到来自35支队伍的119份提交，本文对这些方案进行了分析，结果显示：结合适当的损失函数、异常分数归一化及使用干净机器音和噪声音，无论采用微调预训练模型、使用冻结预训练模型，或从头训练小型模型，各类方法均可取得竞争力表现。
原文摘要 · Abstract (English)
This paper introduces the task description for the Detection and Classification of Acoustic Scenes and Events (DCASE) 2025 Challenge Task 2, titled "First-shot unsupervised anomalous sound detection (ASD) for machine condition monitoring". Building on the DCASE 2024 Challenge Task 2, this task is structured as a first-shot problem within a domain generalization framework. The primary objective of the first-shot approach is to facilitate the rapid deployment of ASD systems for new machine types without requiring machine-specific hyperparameter tunings. For DCASE 2025 Challenge Task 2, sounds from previously unseen machine types have been collected and provided as the evaluation dataset. We received 119 submissions from 35 teams, and an analysis of these submissions has been made in this paper. Analysis showed that various approaches can all be competitive, such as fine-tuning pre-trained models, using frozen pre-trained models, and training small models from scratch, when combined with appropriate cost functions, anomaly score normalization, and use of clean machine and noise sounds.
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