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

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Signal Processing Layers

`cwtLayer`	Continuous wavelet transform (CWT) layer
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer
`stftLayer`	Short-time Fourier transform layer

Feature Extraction

`dlcwt`	Deep learning continuous wavelet transform
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis
`dlstft`	Deep learning short-time Fourier transform
`cwtfilterbank`	Continuous wavelet transform filter bank
`findchangepts`	Find abrupt changes in signal
`findpeaks`	Find local maxima
`modwt`	Maximal overlap discrete wavelet transform
`risetime`	Rise time of positive-going bilevel waveform transitions
`stft`	Short-time Fourier transform
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction
`signalTimeFeatureExtractor`	Streamline signal time feature extraction
`waveletScattering`	Wavelet time scattering

Datastores and Data Import

`edfheader`	Create header structure for EDF or EDF+ file
`edfinfo`	Get information about EDF/EDF+ file
`edfread`	Read data from EDF/EDF+ file
`edfwrite`	Create or modify EDF or EDF+ file
`signalDatastore`	Datastore for collection of signals

Blocks

Wavelet Scattering

Model wavelet scattering network in Simulink

Topics

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.

Featured Examples

Anomaly Detection Using Autoencoder and Wavelets

Use wavelet-extracted features and an autoencoder to detect arc signals in a DC system.

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Detect Anomalies Using Wavelet Scattering with Autoencoders

Learn how to develop an alert system for predictive maintenance using wavelet scattering and deep learning.

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Denoise Speech Using Deep Learning Networks

Denoise speech signals using deep learning networks. The example compares two types of networks applied to the same task: fully connected, and convolutional.

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