14 citations · 32 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
First-Shot Unsupervised Anomalous Sound Detection With Unknown Anomalies Estimated by Metadata-Assisted Audio Generation
Hejing Zhang, Qiaoxi Zhu, Jian Guan +6
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 machine types…
Synth-AC: Enhancing Audio Captioning with Synthetic Supervision
Feiyang Xiao, Qiaoxi Zhu, Jian Guan +4
Data-driven approaches hold promise for audio captioning. However, the development of audio captioning methods can be biased due to the limited availability and quality of text-aud…
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…
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…
Graph Attention for Automated Audio Captioning
Feiyang Xiao, Jian Guan, Qiaoxi Zhu +1
State-of-the-art audio captioning methods typically use the encoder-decoder structure with pretrained audio neural networks (PANNs) as encoders for feature extraction. However, the…