Machine Unsupervised Abnormal Sound Detection Based on Residual Autoencoder-Reference-Cited by-同舟云学术

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Machine Unsupervised Abnormal Sound Detection Based on Residual Autoencoder

Published:2023-11-24 Issue: Volume:59 Page:127-130
ISSN:
Container-title:2023 3rd International Conference on Communication Technology and Information Technology (ICCTIT)
language:
Short-container-title:

Author:

Wei Yi¹,Nong Yingxiong²,Pan Jian³,Xiao Dong⁴,Li Zhe⁵

Affiliation:

1. China Tobacco Guangxi Industrial Co., Ltd,Department of Information Systems Project Management,Nanning,China,530001

2. China Tobacco Guangxi Industrial Co., Ltd,Information Technology Architecture,Nanning,China,530001

3. China Tobacco Guangxi Industrial Co., Ltd,Information System Construction and Operations Division,Nanning,China,530001

4. China Telecom Corporation Limited,,Information Systems Project Management Division, Guangxi Branch,Nanning,China,530001

5. China Tobacco Guangxi Industrial Co., Ltd,Network and Network Information Security Research Division,Nanning,China,530001

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10435702/10435665/10435776.pdf?arnumber=10435776

Reference15 articles.

1. Review of Acoustic Signal-Based Industrial Equipment Fault Diagnosis[J];Zhang;Journal of Computer Engineering & Applications,2023

2. Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions

3. Time-series novelty detection using one-class support vector machines

4. Detection of Abnormal Sound Using Multi-stage GMM for Surveillance Microphone

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