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WVDNet: Time-Frequency Analysis via Semi-Supervised Learning
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DOI：10.1109/LSP.2023.3235646.png)
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Abstract
En 中文
The bilinear based method is one of the commonly used tools in time-frequency analysis (TFA) fields. However, it suffers from the trade-off of high resolution and cross-term interference. We propose WVDNet, a semi-supervised learning model for time-frequency analysis based on the Wigner-Ville distribution (WVD), to reduce the cross-term existing in WVD and relax the requirements of the training data set. The proposed WVDNet is based on the Mean-Teacher model to enable the task model to exploit the unlabeled training data. We first build a synthetic data set for model training, that contains different kinds of amplitude-modulated and frequency-modulated (AM-FM) signals. Next, a task model of WVDNet is designed and the consistency regularization based method is utilized to promote model training. Finally, experiments are conducted on both synthetic and real-world data, showing the effectiveness of suppressing cross-term and strong generalization ability.
Keywords:
Training
Data models
Predictive models
Kernel
Training data
Time-frequency analysis
Task analysis
Deep learning
semi-supervised learning
time-frequency analysis
wigner-ville distribution
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