标题： 基于深度学习和奈曼-皮尔逊引理的无监督异常声音检测 显示英文标题

标题： Unsupervised Detection of Anomalous Sound based on Deep Learning and the Neyman-Pearson Lemma

摘要： 本文提出了一种新颖的优化原理及其在使用自编码器（AE）的声音异常检测（ADS）中的实现方法。 无监督ADS的目标是在没有异常声音训练数据的情况下检测未知的异常声音。 将AE用作正常模型是无监督ADS的最先进技术。 为了降低误报率（FPR），AE被训练以最小化正常声音的重建误差，而异常得分则计算为观察到的声音的重建误差。 不幸的是，由于此训练过程未考虑异常声音的异常得分，真正率（TPR）不一定增加。 在本研究中，我们通过将ADS视为统计假设检验，基于Neyman-Pearson引理定义了一个目标函数。 所提出的目标函数在任意低FPR条件下训练AE以最大化TPR。 为了在目标函数中计算TPR，我们认为异常声音的集合是正常声音的补集，并通过使用拒绝采样算法模拟异常声音。 通过使用合成数据的实验，我们发现所提出的方法在低FPR条件下提高了ADS的性能指标。 此外，我们确认了所提出的方法可以在真实环境中检测异常声音。 显示英文摘要

摘要： This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound (ADS) using an autoencoder (AE). The goal of unsupervised-ADS is to detect unknown anomalous sound without training data of anomalous sound. Use of an AE as a normal model is a state-of-the-art technique for unsupervised-ADS. To decrease the false positive rate (FPR), the AE is trained to minimize the reconstruction error of normal sounds and the anomaly score is calculated as the reconstruction error of the observed sound. Unfortunately, since this training procedure does not take into account the anomaly score for anomalous sounds, the true positive rate (TPR) does not necessarily increase. In this study, we define an objective function based on the Neyman-Pearson lemma by considering ADS as a statistical hypothesis test. The proposed objective function trains the AE to maximize the TPR under an arbitrary low FPR condition. To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm. Through experiments using synthetic data, we found that the proposed method improved the performance measures of ADS under low FPR conditions. In addition, we confirmed that the proposed method could detect anomalous sounds in real environments.