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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 signal attributes, regions, and points of interest

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 Learning Network

This example shows how to use custom autolabeling 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).

Generate Synthetic Signals Using Conditional Generative Adversarial Network

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

Crack Identification From Accelerometer Data

This example shows how to use wavelet and deep learning techniques to detect transverse pavement cracks and localize their position.

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