We propose an outlier robust multivariate time series model which can be used for detecting previously unseen anomalous sounds based on noisy training data. The presented approach doesn't assume the presence of labeled anomalies in the training dataset and uses a novel deep neural network architecture to learn the temporal dynamics of the multivariate time series at multiple resolutions while being robust to contaminations in the training dataset. The temporal dynamics are modeled using recurrent layers augmented with attention mechanism. These recurrent layers are built on top of convolutional layers allowing the network to extract features at multiple resolutions. The output of the network is an outlier robust probability density function modeling the conditional probability of future samples given the time series history. State-of-the-art approaches using other multiresolution architectures are contrasted with our proposed approach. We validate our solution using publicly available machine sound datasets. We demonstrate the effectiveness of our approach in anomaly detection by comparing against several state-of-the-art models.
翻译:我们提议了一个异常强的多变时间序列模型, 可用于根据吵闹的培训数据探测先前不为人知的异常声音。 介绍的方法并不假定培训数据集中存在标签异常现象, 并且使用一种新的深神经网络结构来学习多个分辨率的多变时间序列的时间动态, 同时对培训数据集中的污染保持强力。 时间动态模型使用循环层, 并辅之以关注机制。 这些重复的层建在卷发层的顶端, 使得网络能够在多个分辨率上提取特征。 网络的输出是一个超强的概率密度函数, 根据时间序列历史来模拟未来样本的有条件概率。 使用其他多解析结构的最先进的方法与我们提议的方法形成鲜明对比。 我们用公开可用的机器声音数据集验证我们的解决方案。 我们通过比较一些最先进的模型来显示我们探测异常现象的方法的有效性 。