论文标题
通过合并两个二进制跨凝管来改善异常声音检测的串行方法
Improvement of Serial Approach to Anomalous Sound Detection by Incorporating Two Binary Cross-Entropies for Outlier Exposure
论文作者
论文摘要
异常的声音检测系统必须仅使用普通音频数据检测未知的非典型声音。常规方法使用串行方法，即离群暴露（OE）的组合，该方法对正常和伪反应数据进行了分类并获得嵌入，以及嵌入式建模（IM），该模型（IM）模拟了嵌入的概率分布。尽管由于OE的强大特征提取和IM的鲁棒性，串行方法表现出高性能，但OE仍然存在一个问题，当正常和伪反应数据过于相似或太不同时，OE仍无法正常工作。为了明确区分这些数据，提出的方法在训练OE时使用了两个二进制跨凝管的多任务学习。第一个是损失，将其从中发出的产品发出的目标机器的声音分类，该损失涉及正常数据和伪反应数据太相似的情况。第二个损失是识别声音是否从目标机器发出的损失，该声音涉及正常数据和伪反应数据太差异的情况。我们使用Dcase 2021任务〜2数据集执行实验。我们提出的单模方法优于顶级方法，该方法将多个模型结合在AUC中2.1％。
Anomalous sound detection systems must detect unknown, atypical sounds using only normal audio data. Conventional methods use the serial method, a combination of outlier exposure (OE), which classifies normal and pseudo-anomalous data and obtains embedding, and inlier modeling (IM), which models the probability distribution of the embedding. Although the serial method shows high performance due to the powerful feature extraction of OE and the robustness of IM, OE still has a problem that doesn't work well when the normal and pseudo-anomalous data are too similar or too different. To explicitly distinguish these data, the proposed method uses multi-task learning of two binary cross-entropies when training OE. The first is a loss that classifies the sound of the target machine to which product it is emitted from, which deals with the case where the normal data and the pseudo-anomalous data are too similar. The second is a loss that identifies whether the sound is emitted from the target machine or not, which deals with the case where the normal data and the pseudo-anomalous data are too different. We perform our experiments with DCASE 2021 Task~2 dataset. Our proposed single-model method outperforms the top-ranked method, which combines multiple models, by 2.1% in AUC.
