This paper introduces an active learning (AL) framework for anomalous sound detection (ASD) in machine condition monitoring system. Typically, ASD models are trained solely on normal samples due to the scarcity of anomalous data, leading to decreased accuracy for unseen samples during inference. AL is a promising solution to solve this problem by enabling the model to learn new concepts more effectively with fewer labeled examples, thus reducing manual annotation efforts. However, its effectiveness in ASD remains unexplored. To minimize update costs and time, our proposed method focuses on updating the scoring backend of ASD system without retraining the neural network model. Experimental results on the DCASE 2023 Challenge Task 2 dataset confirm that our AL framework significantly improves ASD performance even with low labeling budgets. Moreover, our proposed sampling strategy outperforms other baselines in terms of the partial area under the receiver operating characteristic score.

翻译：本文提出了一种用于机器状态监测系统中异常声音检测的主动学习框架。通常情况下，由于异常数据的稀缺性，异常声音检测模型仅使用正常样本进行训练，这导致在推理阶段对未见样本的检测精度下降。主动学习通过使模型能够以更少的标注样本有效学习新概念，从而减少人工标注工作量，是解决该问题的有效途径。然而，其在异常声音检测领域的应用效果尚未得到充分探索。为最小化更新成本与时间，本文提出的方法专注于更新异常声音检测系统的评分后端，而无需重新训练神经网络模型。在DCASE 2023挑战赛任务2数据集上的实验结果表明，即使在低标注预算条件下，我们的主动学习框架仍能显著提升异常声音检测性能。此外，我们提出的采样策略在接收者操作特性曲线下部分面积指标上优于其他基线方法。