In this paper, we propose an anomaly detection algorithm for machine sounds with a deep complex network trained by self-supervision. Using the fact that phase continuity information is crucial for detecting abnormalities in time-series signals, our proposed algorithm utilizes the complex spectrum as an input and performs complex number arithmetic throughout the entire process. Since the usefulness of phase information can vary depending on the type of machine sound, we also apply an attention mechanism to control the weights of the complex and magnitude spectrum bottleneck features depending on the machine type. We train our network to perform a self-supervised task that classifies the machine identifier (id) of normal input sounds among multiple classes. At test time, an input signal is detected as anomalous if the trained model is unable to correctly classify the id. In other words, we determine the presence of an anomality when the output cross-entropy score of the multiclass identification task is lower than a pre-defined threshold. Experiments with the MIMII dataset show that the proposed algorithm has a much higher area under the curve (AUC) score than conventional magnitude spectrum-based algorithms.

翻译：本文提出一种基于深度复杂网络的自监督训练机器声音异常检测算法。鉴于相位连续性信息对时域信号异常检测至关重要，本算法采用复数频谱作为输入，并在整个处理过程中执行复数运算。考虑到不同机器声音类型的相位信息有效性存在差异，我们引入注意力机制，根据机器类型动态调整复数频谱与幅度频谱瓶颈特征的权重。我们训练网络执行自监督任务：对正常输入声音的多类机器标识符（id）进行分类。在测试阶段，若训练模型无法正确分类该机器标识符，则判定输入信号为异常。换言之，当多分类任务的输出交叉熵分数低于预设阈值时，即判定存在异常。在MIMII数据集上的实验表明，本算法的曲线下面积（AUC）得分远高于传统基于幅度频谱的算法。