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.
Signal Processing Layers
|Continuous wavelet transform (CWT) layer|
|Maximal overlap discrete wavelet transform (MODWT) layer|
|Short-time Fourier transform layer|
|Deep learning continuous wavelet transform|
|Deep learning maximal overlap discrete wavelet transform and multiresolution analysis|
|Deep learning short-time Fourier transform|
|Continuous wavelet transform filter bank|
|Find abrupt changes in signal|
|Find local maxima|
|Maximal overlap discrete wavelet transform|
|Rise time of positive-going bilevel waveform transitions|
|Short-time Fourier transform|
|Streamline signal frequency feature extraction|
|Streamline signal time feature extraction|
|Wavelet time scattering|
Datastores and Data Import
|Create header structure for EDF or EDF+ file|
|Get information about EDF/EDF+ file|
|Read data from EDF/EDF+ file|
|Create or modify EDF or EDF+ file|
|Datastore for collection of signals|
|Wavelet Scattering||Model wavelet scattering network in Simulink|
- 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.
- 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 Deep Learning Regression with GPU Acceleration (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.