2025
Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection
ICASSP 2025accepted
Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This…