  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.
