We present the task description and discussion on the results of the DCASE 2022 Challenge
Task 2:``Unsupervised anomalous sound detection (ASD) for machine condition monitoring …

We present the task description of the Detection and Classification of Acoustic Scenes and
Events (DCASE) 2023 Challenge Task 2:``First-shot unsupervised anomalous sound …

This paper provides a baseline system for First-shot-compliant unsupervised anomaly
detection (ASD) for machine condition monitoring. First-shot ASD does not allow systems to …

Emitted machine sounds can change drastically due to a change in settings of machines or
varying noise conditions resulting in false alarms when monitoring machine conditions with …

This report proposes anomalous sound detection (ASD) methods using domain
generalization and specialization techniques for the DCASE 2022 Challenge Task 2. We …

This report contains a description of the Fraunhofer FKIE submission for task 2 “First-Shot
Unsupervised Anomalous Sound Detection for Machine Condition Monitoring” of the …

State-of-the-art anomalous sound detection systems often utilize angular margin losses to
learn suitable representations of acoustic data using an auxiliary task, which usually is a …

Different machines can exhibit diverse frequency patterns in their emitted sound. This
feature has been recently explored in anomaly sound detection and reached state-of-the-art …

Using embeddings pre-trained on large datasets as input representations is a popular
approach for classifying audio data in case only a few training samples are available …

We investigate a novel multi-task learning framework that disentangles domain-shared
features and domain-specific features for do-main generalization in anomalous sound …