Zhaoyi Liu

Orcid: 0000-0003-0697-9080

Affiliations:
  • KU Leuven, Belgium


According to our database1, Zhaoyi Liu authored at least 11 papers between 2022 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2025
SCAN: Selective Contrastive Learning Against Noisy Data for Acoustic Anomaly Detection.
IEEE Signal Process. Lett., 2025

ConUAD: Combating Noisy Data through Selective Contrastive Learning for Unsupervised Acoustic Anomaly Detection.
Proceedings of the IEEE Statistical Signal Processing Workshop, 2025

RTASP: Real-Time Actuator and Sensor Platform.
Proceedings of the 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems, 2025

2024
ConvDTW-ACS: Audio Segmentation for Track Type Detection During Car Manufacturing.
CoRR, 2024

Beyond Universal Transformer: Block Reusing with Adaptor in Transformer for Automatic Speech Recognition.
Proceedings of the Advances in Neural Networks - ISNN 2024, 2024

SRAD-CLF: Squeak and Rattle Anomaly Detection via Contrastive Learning Framework on Real Industrial Noise Recordings.
Proceedings of the 32nd European Signal Processing Conference, 2024

2023
FaultBit: Generic and Efficient Wireless Fault Detection Using the Internet of Things.
Proceedings of the Mobile and Ubiquitous Systems: Computing, Networking and Services, 2023

CLF-AIAD: A Contrastive Learning Framework for Acoustic Industrial Anomaly Detection.
Proceedings of the Neural Information Processing - 30th International Conference, 2023

2022
Poster Abstract: Adapting Pretrained Features for Efficient Unsupervised Acoustic Anomaly Detection.
Proceedings of the 21st ACM/IEEE International Conference on Information Processing in Sensor Networks, 2022

CT-SAT: Contextual Transformer for Sequential Audio Tagging.
Proceedings of the 23rd Annual Conference of the International Speech Communication Association, 2022

Unsupervised Acoustic Anomaly Detection Systems Based on Gaussian Mixture Density Neural Network.
Proceedings of the 30th European Signal Processing Conference, 2022


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