Regression
Signal source separation, denoising, signal recovery
Use deep learning techniques to denoise signals. Use differentiable time-frequency transforms to reconstruct signals when there is missing information.
Functions
Blocks
Wavelet Scattering | Model wavelet scattering network in Simulink (Since R2022b) |
Topics
- Denoise Signals with Generative Adversarial Networks
Use autoencoders and generative adversarial networks to denoise signals.
- Manage Data Sets for Machine Learning and Deep Learning Workflows (Signal Processing Toolbox)
Organize, access, and manage data sets for different AI applications.
- Signal Recovery with Differentiable Scalograms and Spectrograms (Wavelet Toolbox)
Use differentiable time-frequency transforms and gradient descent to recover a time-domain signal without the need for phase information. (Since R2022b)
- Signal Source Separation Using W-Net Architecture (Signal Processing Toolbox)
Use a deep learning network to separate two mixed signal sources.
- Denoise EEG Signals Using Differentiable Signal Processing Layers (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.