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Transforms and Spectral Analysis

FFT, DCT, spectral analysis, linear prediction

The frequency-domain representation of a signal reveals important signal characteristics that are difficult to analyze in the time domain. Spectral analysis lets you characterize the frequency content of a signal. The FFT and IFFT System objects and blocks in DSP System Toolbox™ enable you to convert a streaming time-domain signal into the frequency-domain, and vice versa. To compute the spectral estimate of the signal, use the dsp.SpectrumEstimator System object™ in MATLAB® and the Spectrum Estimator block in Simulink®. You can visualize the spectral estimate using the Spectrum Analyzer object and block.

The Spectrum Analyzer in DSP System Toolbox uses the Welch's method of averaging modified periodogram and the filter bank method. Both these methods are FFT-based spectral estimation methods that make no assumptions about the input data and can be used with any kind of signal. For more information on the algorithm the Spectrum Analyzer uses, see Spectral Analysis. To learn how to estimate the power spectral density of a streaming signal in MATLAB, see Estimate the Power Spectrum in MATLAB.


  • Transforms
    Fourier transforms, cosine and wavelet transforms, wavelet scattering
  • Linear Prediction
    Convert linear predictive coefficients (LPC) to cepstral coefficients, LSF, LSP, RC, and vice versa
  • Spectral Analysis
    Parametric and nonparametric methods