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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

Topics

Pedestrian and Bicyclist Classification Using Deep Learning

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

Radar and Communications Waveform Classification Using Deep Learning

This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

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