Anomaly detection (AD) plays a crucial role in various domains, including cybersecurity,
finance, and healthcare, by identifying patterns or events that deviate from normal …

Outlier detection is crucial for improving the performance of machine learning algorithms
and is particularly vital in data sets possessing a small number of points. While the existing …

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 …

For industrial anomalous sound detection (ASD), self-supervised methods have achieved
significant detection performance in many cases. Nevertheless, these methods typically rely …

Unsupervised anomalous sound detection, especially self-supervised methods, plays a
crucial role in differentiating unknown abnormal sounds of machines from normal sounds …

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 …

We address a practical scenario of anomaly detection for industrial sound data, where the
sound of a target machine is corrupted by background noise and interference from …

Boosted by the Internet of Things (IoT) and neural network (NN), an accurate anomalous
sound detection (ASD) system is critical in Industry 4.0 for enabling proactive maintenance …

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

Unsupervised anomalous sound detection (ASD) identifies anomalies by learning or
estimating normal operational patterns and detecting deviations. This capability is crucial for …