Signal Processing Toolbox™ provides functionality to perform signal labeling, feature engineering, and dataset generation for machine learning and deep learning workflows.
|Find abrupt changes in signal|
|Find local maxima|
|Find signal location using similarity search|
|Fourier synchrosqueezed transform|
|Estimate instantaneous frequency|
|Spectral entropy of signal|
|Periodogram power spectral density estimate|
|Spectral kurtosis from signal or spectrogram|
|Analyze signals in the frequency and time-frequency domains|
|Welch’s power spectral density estimate|
Radar Waveform Classification Using Deep Learning (Phased Array System Toolbox)
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).
Pedestrian and Bicyclist Classification Using Deep Learning (Phased Array System Toolbox)
This example shows how to classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.
Classify human electrocardiogram signals using wavelet-based feature extraction and a support vector machine classifier.
Classify ECG signals using the continuous wavelet transform and a deep convolutional neural network.