When training a model for anomalous sound detection, one usually needs to estimate the
underlying distribution of the normal data. By doing so, anomalous data has a lower …

Systems based on sub-cluster AdaCos yield state-of-the-art performance on the DCASE
2020 dataset for anomalous sound detection. In contrast to the previous year, the dataset …

Anomalous sound detection systems based on sub-cluster AdaCos yield state-of-the-art
performance on the DCASE 2020 dataset for anomalous sound detection. In contrast to the …

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

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

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 …

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 …

Anomalous sound detection (ASD) is one of the most important fields in industrial facility
maintenance. For this task, semi-supervised approaches are preferred thanks to their …

In this paper, we propose an effective anomalous sound detection (ASD) method based on
representation learning with simulated anomalies. Recently, ASD systems have used Outlier …

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 …