用少量异常数据提升声音异常检测精度，解决真实异常数据难获取问题。
Serial-OE: Anomalous sound detection based on serial method with outlier exposure capable of using small amounts of anomalous data for training
- 结合正常数据与伪异常数据训练，支持小量真实异常数据注入。
- 在DCASE2020数据集上超越现有最佳模型，小样本下仍有效。
- 适用于运维阶段动态更新的工业声音监测系统。
我们提出Serial-OE，一种基于序列方法的异常声音检测新范式，能够利用少量异常数据提升性能。传统方法因异常数据采集成本高，主要依赖正常数据建模。本方法通过引入异常暴露框架，结合正常数据与伪异常数据进行训练，并可融合少量真实异常数据。在DCASE2020 Task2数据集上的全面评估显示，该方法优于当前最优的ASD模型。我们还研究了训练中使用少量异常数据、无设备ID信息数据以及污染数据对性能的影响。实验表明，极少量异常数据即可显著改善仅用正常数据训练的局限性。本研究为可在运行阶段动态加入异常数据的ASD系统提供了可行方案，推动更精准的异常检测发展。
原文摘要 · Abstract (English)
We introduce Serial-OE, a new approach to anomalous sound detection (ASD) that leverages small amounts of anomalous data to improve the performance. Conventional ASD methods rely primarily on the modeling of normal data, due to the cost of collecting anomalous data from various possible types of equipment breakdowns. Our method improves upon existing ASD systems by implementing an outlier exposure framework that utilizes normal and pseudo-anomalous data for training, with the capability to also use small amounts of real anomalous data. A comprehensive evaluation using the DCASE2020 Task2 dataset shows that our method outperforms state-of-the-art ASD models. We also investigate the impact on performance of using a small amount of anomalous data during training, of using data without machine ID information, and of using contaminated training data. Our experimental results reveal the potential of using a very limited amount of anomalous data during training to address the limitations of existing methods using only normal data for training due to the scarcity of anomalous data. This study contributes to the field by presenting a method that can be dynamically adapted to include anomalous data during the operational phase of an ASD system, paving the way for more accurate ASD.
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