Signal Processing Toolbox™ provides functions and apps that enable you to visualize and compare time-frequency content of nonstationary signals. Compute the short-time Fourier transform and its inverse. Obtain sharp spectral estimates using reassignment or Fourier synchrosqueezing. Plot cross-spectrograms, Wigner-Ville distributions, and persistence spectra. Extract and track time-frequency ridges. Estimate instantaneous frequency, spectral kurtosis, and spectral entropy. Perform data-adaptive time-frequency analysis using empirical mode decomposition and the Hilbert-Huang transform.
|Empirical mode decomposition|
|Fourier synchrosqueezed transform|
|Inverse Fourier synchrosqueezed transform|
|Estimate instantaneous frequency|
|Visualize spectral kurtosis|
|Spectral kurtosis from signal or spectrogram|
|Spectral entropy of signal|
|Analyze signals in the frequency and time-frequency domains|
|Spectrogram using short-time Fourier transform|
|Cross-spectrogram using short-time Fourier transforms|
|Short-time Fourier transform|
|Signal reconstruction from STFT magnitude|
|Determine whether window-overlap combination is COLA compliant|
|Inverse short-time Fourier transform|
|Variational mode decomposition|
|Wigner-Ville distribution and smoothed pseudo Wigner-Ville distribution|
|Cross Wigner-Ville distribution and cross smoothed pseudo Wigner-Ville distribution|
Examine the features and limitations of the time-frequency analysis functions provided by Signal Processing Toolbox.
Practical Introduction to Continuous Wavelet Analysis (Wavelet Toolbox)
This example shows how to perform and interpret continuous wavelet analysis.
Display the spectrogram of a linear FM signal.
Compute the instantaneous frequency of a signal using the Fourier synchrosqueezed transform.
Compute the instantaneous frequency of two sinusoids using the Fourier synchrosqueezed transform. Determine how separated the sinusoids must be for the transform to resolve them.
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.