Abstract:

We investigate a novel multi-task learning framework that disentangles domain-shared features and domain-specific features for do- main generalization in anomalous sound detection. Disentanglement leads to better latent features and also increases flexibility in post-processing due to the availability of multiple embedding spaces. The framework was at the core of our submissions to the DCASE2022 Challenge Task 2. We ranked 5th out of 32 teams in the competition, obtaining an overall harmonic mean of 67.57% on the blind evaluation set, surpassing the baseline by 13.5% and trailing the top rank by 3.4%. We also explored machine-specific loss functions and domain generalization methods, which showed improvements on the development set, but were less effective on the evaluation set.