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.
|Signal Labeler||Label signal attributes, regions, and points of interest|
|Create labeled signal set|
|Create signal label definition|
|Modify and convert signal masks and extract signal regions of interest|
|Count number of unique labels|
|Get list of labels from folder names|
|Find indices to split labels according to specified proportions|
|Datastore for collection of signals|
|Deep learning short-time Fourier transform|
|Short-time Fourier transform layer|
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.
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.
Generate a MEX function and a standalone executable to perform waveform segmentation on a Raspberry Pi™.
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).
This example shows the workflow to classify human electrocardiogram (ECG) signals using the Continuous Wavelet Transform (CWT) and a deep convolutional neural network (CNN).