In industry, machine anomalous sound detection (ASD) is in great demand. However, collecting enough abnormal samples is difficult due to the high cost, which boosts the rapid development of unsupervised ASD algorithms. Autoencoder (AE) based methods have been widely used for unsupervised ASD, but suffer from problems including 'shortcut', poor anti-noise ability and sub-optimal quality of features. To address these challenges, we propose a new AE-based framework termed AEGM. Specifically, we first insert an auxiliary classifier into AE to enhance ASD in a multi-task learning manner. Then, we design a group-based decoder structure, accompanied by an adaptive loss function, to endow the model with domain-specific knowledge. Results on the DCASE 2021 Task 2 development set show that our methods achieve a relative improvement of 13.11% and 15.20% respectively in average AUC over the official AE and MobileNetV2 across test sets of seven machines.

翻译：工业领域中，机器异常声音检测（ASD）具有巨大需求。然而，由于高成本导致难以收集足够的异常样本，这推动了无监督ASD算法的快速发展。基于自编码器（AE）的方法已广泛应用于无监督ASD，但存在“捷径”、抗噪能力差以及特征质量次优等问题。为解决这些挑战，我们提出一种名为AEGM的新型AE框架。具体而言，我们首先在AE中插入辅助分类器，通过多任务学习方式增强ASD性能。随后设计基于分组解码器的结构，配合自适应损失函数，使模型具备领域特定知识。在DCASE 2021 Task 2开发集上的实验结果表明，在七类机器的测试集上，我们的方法相较于官方AE和MobileNetV2，在平均AUC上分别实现了13.11%和15.20%的相对提升。