With the development of multi-modal man-machine interaction, audio signal analysis is
gaining importance in a field traditionally dominated by video. In particular, anomalous …

Representation learning, using self-supervised classification has recently been shown to
give state-of-the-art accuracies for anomaly detection on computer vision datasets …

With the emergence of new digital technologies, a significant surge has been seen in the
volume of multimedia data generated from various smart devices. Several challenges for …

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

In critical infrastructures, timely detection of anomalies is essential to detect failures, avoid
catastrophic damages, and improve resilience. Neural Network models are one of the state …

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 …

A typical approach for unsupervised anomaly detection of machine sounds learns an
autoencoder model for reconstructing the spectrograms of normal sounds. During inference …

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

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

Detecting anomalies in sound data has recently received significant attention due to the
increasing number of implementations of sound condition monitoring solutions for critical …