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Signal Processing Using Deep Learning

Extend deep learning workflows with signal processing applications

Apply deep learning to signal processing by using Deep Learning Toolbox™ together with Signal Processing Toolbox™ or Wavelet Toolbox™. For audio and speech processing applications, see Audio Processing Using Deep Learning. For applications in wireless communications, see Wireless Communications Using Deep Learning.

Apps

Signal LabelerLabel signal attributes, regions, and points of interest

Functions

labeledSignalSetCreate labeled signal set
signalLabelDefinitionCreate signal label definition
signalMaskModify and convert signal masks and extract signal regions of interest
countlabelsCount number of unique labels
folders2labelsGet list of labels from folder names
splitlabelsFind indices to split labels according to specified proportions
signalDatastoreDatastore for collection of signals
dlstftDeep learning short-time Fourier transform
stftLayerShort-time Fourier transform layer

Topics

Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Radar and Communications Waveform Classification Using Deep Learning (Radar Toolbox)

Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Automate Signal Labeling with Custom Functions (Signal Processing Toolbox)

Use Signal Labeler to locate and label QRS complexes and R peaks of ECG signals.

Crack Identification From Accelerometer Data (Wavelet Toolbox)

Use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.

Deploy Signal Segmentation Deep Network on Raspberry Pi

Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.

Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning

This example shows how to generate and deploy a CUDA® executable that classifies human electrocardiogram (ECG) signals using features extracted by the continuous wavelet transform (CWT) and a pretrained convolutional neural network (CNN).

Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi

This example shows the workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN).

Featured Examples

Waveform Segmentation Using Deep Learning

Waveform Segmentation Using Deep Learning

Segment human electrocardiogram (ECG) signals using recurrent deep learning networks and time-frequency analysis.

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Classify ECG Signals Using Long Short-Term Memory Networks

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using deep learning and signal processing. In particular, the example uses Long Short-Term Memory networks and time-frequency analysis.

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Classify Time Series Using Wavelet Analysis and Deep Learning

Classify Time Series Using Wavelet Analysis and Deep Learning

Classify human electrocardiogram (ECG) signals using the continuous wavelet transform (CWT) and a deep convolutional neural network (CNN).

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Generate Synthetic Signals Using Conditional GAN

Generate Synthetic Signals Using Conditional GAN

Use a conditional generative adversarial network to produce synthetic data for model training.

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Learn Pre-Emphasis Filter Using Deep Learning

Learn Pre-Emphasis Filter Using Deep Learning

Use a convolutional deep network to learn a pre-emphasis filter for speech recognition.

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Denoise EEG Signals Using Deep Learning Regression

Denoise EEG Signals Using Deep Learning Regression

Remove EOG noise from EEG signals using deep learning regression.

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Hand Gesture Classification Using Radar Signals and Deep Learning

Hand Gesture Classification Using Radar Signals and Deep Learning

Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.

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Parasite Classification Using Wavelet Scattering and Deep Learning

Parasite Classification Using Wavelet Scattering and Deep Learning

Classify parasitic infections using wavelet image scattering and deep learning.

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Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

Fault Detection Using Wavelet Scattering and Recurrent Deep Networks

Classify faults in acoustic recordings of air compressors using a wavelet scattering network paired with a recurrent neural network.

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Anomaly Detection Using Autoencoder and Wavelets

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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Deep Learning Toolbox Documentation

Support

Introducing Deep Learning with MATLAB

Introducing Deep Learning with MATLAB

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