提出自适应时序池化方法，提升无训练异常声音检测效果
Temporal Pooling Strategies for Training-Free Anomalous Sound Detection with Self-Supervised Audio Embeddings
- 设计相对偏差池化，动态加权异常程度高的音段
- 在5个数据集上超越均值池化，达当前最优性能
- 适合无需微调的工业异常声音实时监测场景
基于预训练音频嵌入的无训练异常声音检测（ASD）近年来受到广泛关注，因其仅需正常参考数据即可检测异常声音，无需任务特定模型训练或微调。然而，现有嵌入式方法几乎全部依赖时序均值池化，时序池化在无训练ASD中尚未被充分探索。本文首次系统评估了预训练音频嵌入在无训练ASD中的时序池化策略。提出相对偏差池化（RDP），一种自适应池化方法，对具有更强时序偏离的嵌入赋予更大权重；研究使用广义均值（GeM）池化的特征级非线性聚合；并考察两者的混合组合。在五个基准数据集上的实验表明，所提池化策略持续优于均值池化，并在无训练ASD中达到最先进水平，包括在DCASE2025 ASD数据集上超越此前报告的训练系统和集成模型。
原文摘要 · Abstract (English)
Training-free anomalous sound detection (ASD) based on pre-trained audio embedding models has recently garnered significant attention, as it enables the detection of anomalous sounds using only normal reference data without task-specific model training or fine-tuning. However, existing embedding-based approaches almost exclusively rely on temporal mean pooling, leaving temporal pooling in training-free ASD largely unexplored. In this paper, we present the first systematic evaluation of temporal pooling strategies for training-free ASD with pre-trained audio embeddings. We propose relative deviation pooling (RDP), an adaptive pooling method that assigns larger weights to embeddings with stronger temporal deviations, investigate feature-wise non-linear aggregation using generalized mean (GeM) pooling, and examine a hybrid combination of both strategies. Experiments on five benchmark datasets demonstrate that the proposed pooling strategies consistently outperform mean pooling and achieve state-of-the-art performance for training-free ASD, including results that surpass previously reported trained systems and ensembles on the DCASE2025 ASD dataset.
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