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.

Funzioni

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Autoencoders

deepSignalAnomalyDetector Create signal anomaly detector (Da R2023a)

Signal Processing Layers

`cwtLayer`	Continuous wavelet transform (CWT) layer (Da R2022b)
`modwtLayer`	Maximal overlap discrete wavelet transform (MODWT) layer (Da R2022b)
`stftLayer`	Short-time Fourier transform layer (Da R2021b)
`istftLayer`	Inverse short-time Fourier transform layer (Da R2024a)

Feature Extraction

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

Datastores and Data Management

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

Blocchi

Wavelet Scattering

Model wavelet scattering network in Simulink (Da R2022b)

Argomenti

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 Differentiable Signal Processing Layers (Signal Processing Toolbox)
Remove EOG noise from EEG signals using deep learning regression.

Esempi in primo piano

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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