1 citations · 1 across the 5 of their papers we have counts for
5 papers
Transformer-based Autoencoder with ID Constraint for Unsupervised Anomalous Sound Detection
Jian Guan, Youde Liu, Qiuqiang Kong +4
Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised l…
Anomalous Sound Detection Using Self-Attention-Based Frequency Pattern Analysis of Machine Sounds
Hejing Zhang, Jian Guan, Qiaoxi Zhu +2
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 pe…
Time-weighted Frequency Domain Audio Representation with GMM Estimator for Anomalous Sound Detection
Jian Guan, Youde Liu, Qiaoxi Zhu +3
Although deep learning is the mainstream method in unsupervised anomalous sound detection, Gaussian Mixture Model (GMM) with statistical audio frequency representation as input can…
Anomalous Sound Detection using Audio Representation with Machine ID based Contrastive Learning Pretraining
Jian Guan, Feiyang Xiao, Youde Liu +2
Existing contrastive learning methods for anomalous sound detection refine the audio representation of each audio sample by using the contrast between the samples' augmentations (e…
Anomalous Sound Detection using Spectral-Temporal Information Fusion
Youde Liu, Jian Guan, Qiaoxi Zhu +1
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 stable and per…