Signal Processing Using Deep Learning

Extend deep learning workflows with signal processing and communications applications

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

Apps

Signal LabelerLabel signals for analysis or machine and deep learning applications

Topics

Classify ECG Signals Using Long Short-Term Memory Networks

This example shows how to classify heartbeat electrocardiogram (ECG) data from the PhysioNet 2017 Challenge using deep learning and signal processing.

Classify Time Series Using Wavelet Analysis and Deep Learning

This example shows how to classify human electrocardiogram (ECG) signals using the continuous wavelet transform (CWT) and a deep convolutional neural network (CNN).

Modulation Classification with Deep Learning

This example shows how to use a convolutional neural network (CNN) for modulation classification.

Waveform Segmentation Using Deep Learning

This example shows how to segment human electrocardiogram (ECG) signals using recurrent deep learning networks and time-frequency analysis.

Label QRS Complexes and R Peaks of ECG Signals Using Deep Network

This example shows how to use custom automated labeling functions in Signal Labeler to label QRS complexes and R peaks of electrocardiogram (ECG) signals.

Pedestrian and Bicyclist Classification Using Deep Learning

This example shows how to classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.

Radar Waveform Classification Using Deep Learning

This example shows how to classify radar waveform types of generated synthetic data using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

Featured Examples