First-Shot Unsupervised Anomalous Sound Detection Framework with Metadata-Assisted Audio Generation
The author proposes a new framework for first-shot unsupervised anomalous sound detection using metadata-assisted audio generation to estimate unknown anomalies, achieving competitive performance in the DCASE 2023 Challenge Task 2.
AdaProj: Adaptive Angular Margin Subspace Projections for Anomalous Sound Detection
AdaProj introduces a novel loss function for learning class-specific subspaces, outperforming other methods in anomalous sound detection.
ADAPROJ: Adaptive Angular Margin Subspace Projections for Anomalous Sound Detection
AdaProj introduces a novel loss function for learning class-specific subspaces, outperforming other methods in anomalous sound detection.
Distributed Collaborative Anomalous Sound Detection by Embedding Sharing
Collaborative method using embeddings improves anomalous sound detection.
Enhancing Discriminative Feature Extraction for Anomaly Detection in Unlabeled Machine Sound Conditions
The authors propose improvements to the discriminative feature extraction approach for anomalous sound detection in unlabeled conditions, including enhanced feature extractors and effective pseudo-labeling methods.