Anomalous Sound Detection Using Audio Representation with Machine ID Based Contrastive Learning Pretraining-Reference-Cited by-同舟云学术

Anomalous Sound Detection Using Audio Representation with Machine ID Based Contrastive Learning Pretraining

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Guan Jian¹,Xiao Feiyang¹,Liu Youde²,Zhu Qiaoxi³,Wang Wenwu⁴

Affiliation:

1. Harbin Engineering University,Group of Intelligent Signal Processing, College of Computer Science and Technology,Harbin,China,150001

2. Harbin Institute of Technology,School of Computer Science and Technology,Harbin,China,150001

3. University of Technology Sydney,Centre for Audio, Acoustics and Vibration,Ultimo,NSW,Australia

4. University of Surrey,Centre for Vision Speech and Signal Processing,Guildford,UK,GU2 7XH

Funder

Natural Science Foundation of Heilongjiang Province

British Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10096054.pdf?arnumber=10096054

Reference19 articles.

1. Self-Supervised Acoustic Anomaly Detection Via Contrastive Learning

2. Supervised contrastive learning;khosla;Proc of Advances in Neural Information Processing Systems (NIPS),2020

3. ArcFace: Additive Angular Margin Loss for Deep Face Recognition

4. A contrastive semi-supervised learning framework for anomaly sound detection;cai;Proc of Detection and Classification of Acoustic Scenes and Events Workshop (DCASE),2021

5. A simple framework for contrastive learning of visual representations;chen;Proc of the International Conference on Machine Learning (ICML),2020

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