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 Labeler

Label signal attributes, regions, and points of interest

Functions

`labeledSignalSet`	Create labeled signal set
`signalLabelDefinition`	Create signal label definition
`signalMask`	Modify and convert signal masks and extract signal regions of interest
`countlabels`	Count number of unique labels
`folders2labels`	Get list of labels from folder names
`splitlabels`	Find indices to split labels according to specified proportions
`signalDatastore`	Datastore for collection of signals
`dlstft`	Deep learning short-time Fourier transform
`stftLayer`	Short-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

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 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.

Open Live Script