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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Autoencoders

deepSignalAnomalyDetector Create signal anomaly detector (Since R2023a)

Signal Processing Layers

`cwtLayer`	Continuous wavelet transform layer (Since R2022b)
`icwtLayer`	Inverse continuous wavelet transform layer (Since R2024b)
`modwtLayer`	Maximal overlap discrete wavelet transform layer (Since R2022b)
`stftLayer`	Short-time Fourier transform layer (Since R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Since R2024a)

Feature Extraction

`cwtmag2sig`	Signal reconstruction from CWT magnitude (Since R2023b)
`dlcwt`	Deep learning continuous wavelet transform (Since R2022b)
`dlicwt`	Deep learning inverse continuous 1-D wavelet transform (Since R2024b)
`dlmodwt`	Deep learning maximal overlap discrete wavelet transform and multiresolution analysis (Since R2022a)
`dlstft`	Deep learning short-time Fourier transform (Since R2021a)
`dlistft`	Deep learning inverse short-time Fourier transform (Since R2024a)
`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
`stftmag2sig`	Signal reconstruction from STFT magnitude (Since R2020b)
`signalFrequencyFeatureExtractor`	Streamline signal frequency feature extraction (Since R2021b)
`signalTimeFeatureExtractor`	Streamline signal time feature extraction (Since R2021a)
`signalTimeFrequencyFeatureExtractor`	Streamline signal time-frequency feature extraction (Since R2024a)
`waveletScattering`	Wavelet time scattering

Datastores and Data Management

`edfheader`	Create header structure for EDF or EDF+ file (Since R2021a)
`edfinfo`	Get information about EDF/EDF+ file (Since R2020b)
`edfread`	Read data from EDF/EDF+ file (Since R2020b)
`edfwrite`	Create or modify EDF or EDF+ file (Since R2021a)
`paddata`	Pad data by adding elements (Since R2023b)
`resize`	Resize data by adding or removing elements (Since R2023b)
`trimdata`	Trim data by removing elements (Since R2023b)
`signalDatastore`	Datastore for collection of signals (Since R2020a)

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.

Featured Examples

Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences

Detect anomalies in acoustic data using wavelet scattering with the deepSignalAnomalyDetector object.

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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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