Multi-branch Network with Cross-Domain Feature Fusion for Anomalous Sound Detection

Anomalous sound detection (ASD) is a key technology to identify abnormal sounds in various industries. Self-supervised anomalous sound detection aims at detecting unknown machine anomalous sounds by learning the characteristics of the normal sounds using metainformation. In this paper, we propose a multi-branch network with cross-domain feature fusion (MBN-CFF) for self-supervised ASD task. The multi-branch network splits the complete feature representations and feeds them individually into classifiers to generate category predictions. The weighted loss, calculated by multiple predictions and the real labels, guides the model training process. We also design a cross-domain feature fusion (CFF) block for effectively fusing the time-domain and frequency-domain features and an attentive sandglass (AS) block for effectively extracting features. Experimental results on the DCASE2020 challenge task 2 show that our MBN-CFF network achieves the best performance with the AUC score of 94.73% and pAUC score of 88.74%, respectively, compared to the other five existing methods for anomalous sound detection. The results of ablation experiments show the effectiveness of CFF and AS blocks, multi-brach prediction (MBP).