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应用深度学习方法对信号进行去噪。当存在缺失信息时，使用可微分时频变换来重新构造信号。
App
函数
模块
| Wavelet Scattering | Simulink 中的小波散射网络建模 (自 R2022b 起) |
相关信息
主题
- Use Experiment Manager Templates for Signal Processing Workflows (Signal Processing Toolbox)
Set up and run deep learning experiments for signal segmentation, classification, and regression.
- Signal Segmentation by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification by Sweeping Hyperparameters (Signal Processing Toolbox)
- Signal Classification Using Transfer Learning (Signal Processing Toolbox)
- Signal Regression by Sweeping Hyperparameters (Signal Processing Toolbox)
精选示例
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
Signal Source Separation Using W-Net Architecture
Use a deep learning network to separate two mixed signal sources.
Denoise EEG Signals Using Differentiable Signal Processing Layers
Remove EOG noise from EEG signals using deep learning regression.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
使用深度学习网络对语音去噪
此示例说明如何使用深度学习网络对语音信号去噪。该示例比较应用于同一任务的两种类型的网络：全连接网络和卷积网络。
Denoise Signals with Adversarial Learning Denoiser Model
Use an adversarial learning denoiser model to denoise signals.
Signal Recovery with Differentiable Scalograms and Spectrograms
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information.
