Hierarchical Feature Disentanglement for Anomalous Sound Detection
Gradient Reversal-based Hierarchical feature Disentanglement (GRHD) for anomalous sound detection under domain shift: a gradient reversal classifier strips domain-unrelated information from learned features, while a hierarchical metadata structure (section IDs organized over machine and recording attribute groups) guides fine-grained, domain-specific feature learning. Evaluated on the DCASE 2022 Challenge Task 2 machine-sound dataset with AUC/pAUC gains over parallel section-ID/attribute training (ICASSP 2025; arXiv:2501.01604).
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Disentangling Hierarchical Features for Anomalous Sound Detection Under Domain Shift
Anomalous sound detection (ASD) encounters difficulties with domain shift, where the sounds of machines in target domains differ significantly from those in source domains due to varying operating co…