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, instantaneous bandwidth, spectral kurtosis, and spectral entropy. Perform data-adaptive time-frequency analysis using empirical or variational mode decomposition and the Hilbert-Huang transform.
|Fourier synchrosqueezed transform|
|Inverse Fourier synchrosqueezed transform|
|Estimate instantaneous bandwidth|
|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|
|Deep learning short-time Fourier transform|
|Short-time Fourier transform layer|
|Signal reconstruction from STFT magnitude|
|Determine whether window-overlap combination is COLA compliant|
|Inverse short-time Fourier transform|
|Wigner-Ville distribution and smoothed pseudo Wigner-Ville distribution|
|Cross Wigner-Ville distribution and cross smoothed pseudo Wigner-Ville distribution|
- Time-Frequency Gallery
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.
- FFT-Based Time-Frequency Analysis
Display the spectrogram of a linear FM signal.
- Instantaneous Frequency of Complex Chirp
Compute the instantaneous frequency of a signal using the Fourier synchrosqueezed transform.
- Detect Closely Spaced Sinusoids
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 and Communications Waveform Classification Using Deep Learning (Phased Array System Toolbox)
This example shows how to classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
- 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.