WVDNet: Time-Frequency Analysis via Semi-Supervised Learning-Reference-Cited by-同舟云学术

WVDNet: Time-Frequency Analysis via Semi-Supervised Learning

Published:2023 Issue: Volume:30 Page:55-59
ISSN:1070-9908
Container-title:IEEE Signal Processing Letters
language:
Short-container-title:IEEE Signal Process. Lett.

Author:

Liu Naihao¹^ORCID,Wang Jingyu²^ORCID,Yang Yang¹^ORCID,Li Zhen¹,Gao Jinghuai¹^ORCID

Affiliation:

1. School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an, China

2. School of Software Engineering, Xi'an Jiaotong University, Xi'an, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Electrical and Electronic Engineering,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/97/10036333/10012547.pdf?arnumber=10012547

Reference26 articles.

1. A highly adaptive directional time–frequency distribution

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3. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;tarvainen;Proc 31st Int Conf Neural Inf Process Syst,0

4. Crossterm-Free Time-Frequency Analyses Exploiting Deep Neural Networks

5. The use of cone-shaped kernels for generalized time-frequency representations of nonstationary signals

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