# BISPECTRUM MATLAB PDF

Generalizations[ edit ] Bispectra fall in the category of higher-order spectra, or polyspectra and provide supplementary information to the power spectrum. The third order polyspectrum bispectrum is the easiest to compute, and hence the most popular. A statistic defined analogously is the bispectral coherency or bicoherence. Applications[ edit ] "Bispectral analysis" redirects here. For the speckle imaging method, see Speckle masking.

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Example: seconds 1 is a duration scalar representing a 1-second time difference between consecutive signal samples. Use this option to analyze the frequency content of a stationary signal. For more information, Spectrum Computation. Use this option to analyze how the frequency content of a signal changes over time. For more information, see Spectrogram Computation. Use this option to visualize the fraction of time that a particular frequency component is present in a signal.

For more information, see Persistence Spectrum Computation. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1, By default, pspectrum computes the spectrum over the whole Nyquist range: If the specified frequency band contains a region that falls outside the Nyquist range, then pspectrum truncates the frequency band.

If the specified frequency band lies completely outside of the Nyquist range, then pspectrum throws an error. See Spectrum Computation for more information about the Nyquist range. If x is nonuniformly sampled, then pspectrum linearly interpolates the signal to a uniform grid and defines an effective sample rate equal to the inverse of the median of the differences between adjacent time points. Example: [0. The default value of this argument depends on the size of the input data.

See Spectrogram Computation for details. A small leakage value finds small tones in the vicinity of larger tones, but smears close frequencies together. The default value of this argument depends on the spectral window. If this option is set to true, then pspectrum sharpens the localization of spectral estimates by performing time and frequency reassignment. The reassignment technique produces periodograms and spectrograms that are easier to read and interpret.

The technique provides exact localization for chirps and impulses. This argument controls the duration of the segments used to compute the short-time power spectra that form spectrogram or persistence spectrum estimates.

The default value of this argument depends on the size of the input data and, if it was specified, the frequency resolution. To conserve the total power, the function multiples the power by 2 at all frequencies except 0 and the Nyquist frequency. This option is valid only for real signals. Output Arguments p — Spectrum vector matrix Spectrum, returned as a vector or a matrix.

For example, the average power of a sinusoid is one-half the square of the sinusoid amplitude. If the input signal contains time information, then f contains frequencies expressed in Hz. If the input does not have time information, then t contains sample numbers.

If the input to pspectrum is a timetable, then t has the same format as the time values of the input timetable. If the input to pspectrum is a numeric vector sampled at a set of time instants specified by a numeric, duration.

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