用于判别式异常声音检测的伪标签蒸馏
Pseudo-label distillation for discriminative anomalous sound detection
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中文总结 AI 辅助
研究针对判别式异常声音检测方法依赖详细标签成本高、基于SSL的无标签方法模型大计算昂贵的问题，提出伪标签蒸馏框架及NRFT方法，在相关数据集上评估分析，结果表明该框架能转移并提升性能，NRFT方法也有增益。
中文摘要 AI 辅助
判别式异常声音检测（ASD）方法通过使用机器信息标签的分类任务来训练特征提取器，然后基于与正常样本的距离在生成的特征空间中检测异常，该方法有效捕获机器特征，但依赖详细标签成本高。基于自监督学习（SSL）的无标签方法虽有竞争力但模型大且计算昂贵。为此提出简单的伪标签蒸馏框架，从SSL特征生成伪标签并训练紧凑判别特征提取器，还提出轻量级噪声鲁棒特征变换（NRFT）方法抑制噪声影响。在DCASE 2020 - 2025任务2数据集上用四个SSL模型进行综合评估分析，结果表明伪标签蒸馏不仅能将SSL模型性能转移到紧凑模型，还能利用可用粗标签和数据增强进一步提升性能，NRFT方法也有增益。
英文摘要
Discriminative anomalous sound detection (ASD) methods train a feature extractor through a classification task using machine-information labels. They then detect anomalies in the resulting feature space based on distances to normal samples. The discriminative feature space effectively captures machine characteristics, leading to high ASD performance. However, this approach benefits from detailed labels, which are costly to obtain. An alternative is a self-supervised learning (SSL)-based label-free approach. This approach directly uses SSL features for ASD and has shown competitive performance. However, SSL models are typically large and computationally expensive. To address these problems, we propose a simple pseudo-label distillation framework. The proposed method generates pseudo labels from SSL features and trains a compact discriminative feature extractor using these pseudo labels. To suppress the effect of noise on pseudo-label generation, we also propose lightweight noise-robust feature transformation (NRFT) methods utilizing a small amount of clean machine-sound data or isolated noise data. We conducted comprehensive evaluations and analyses on the DCASE 2020-2025 Task 2 datasets using four SSL models. The results demonstrate that pseudo-label distillation not only transfers the performance of SSL models to a compact model but also further improves performance by leveraging available coarse labels and data augmentation. Also, our NRFT methods provide further gains.
