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Spectral Analysis

Power spectrum, coherence, time-frequency analysis, windows

Signal Processing Toolbox™ provides a family of spectral analysis functions and apps that let you characterize the frequency content of a signal. FFT-based nonparametric methods, such as Welch’s method or the periodogram, make no assumptions about the input data and can be used with any kind of signal. Parametric and subspace methods, such as Burg’s, covariance, and MUSIC, incorporate prior knowledge of the signal and can yield more accurate spectral estimates.

Compute power spectra of nonuniformly sampled signals or signals with missing samples using the Lomb-Scargle method. Analyze nonstationary signals using time-frequency techniques like the spectrogram and measure signal similarities in the frequency domain by estimating their spectral coherence. Design and analyze Hamming, Kaiser, Gaussian, and other data windows.

  • Spectral Estimation
    Periodogram, Welch, and Lomb-Scargle PSD, coherence, transfer function, frequency reassignment
  • Time-Frequency Analysis
    Spectrogram, cross-spectrogram, Fourier synchrosqueezing, time-frequency reassignment
  • Parametric Spectral Estimation
    Burg, Yule-Walker, covariance, and modified covariance methods
  • Subspace Methods
    Frequency and pseudospectrum estimates, multiple signal classification (MUSIC), root MUSIC
  • Windows
    Hamming, Blackman, Bartlett, Chebyshev, Taylor, Kaiser
  • Spectral Measurements
    Channel power, bandwidth, mean frequency, median frequency, harmonic distortion

Featured Examples

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