Although deep learning is the mainstream method in unsupervised anomalous sound
detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as …

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task
introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target …

This technical report describes our submission for DCASE 2023 challenge task 2. To
address the first-shot and domain shift problem in anomalous sound detection (ASD), we …

Unsupervised anomalous sound detection aims to detect unknown abnormal sounds of
machines from normal sounds. However, the state-of-the-art approaches are not always …

In this work, we thoroughly evaluate the efficacy of pretrained neural networks as feature
extractors for anomalous sound detection. In doing so, we leverage the knowledge that is …

Existing contrastive learning methods for anomalous sound detection refine the audio
representation of each audio sample by using the contrast between the samples' …

An anomalous sound detection system to detect unknown anomalous sounds usually needs
to be built using only normal sound data. Moreover, it is desirable to improve the system by …

Unsupervised anomalous sound detection (ASD) aims to identify anomalous sounds by
learning the features of normal operational sounds and sensing their deviations. Recent …

Anomalous Sound Detection (ASD) aims to identify whether the sound emitted from a
machine is anomalous or not. Most advanced methods use 2-D CNNs to extract features of …

In this paper, we address the problem of detecting previously unseen anomalous audio
events, when the training dataset itself does not contain any examples of anomalies. While …