Anomalous Sound Detection Using Representational Learning and Source Separation with CMGAN
This research paper proposes a novel method for anomalous sound detection in machine condition monitoring by training a neural network to separate non-target machine sounds, leading to improved representation learning and outperforming traditional auto-encoder and target separation approaches.
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.
Distributed Collaborative Anomalous Sound Detection by Embedding Sharing
Collaborative method using embeddings improves 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.
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.
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.
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