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Owning Class: spectral analysis
Requires: MathScript RT Module
pspec_music(x, order)
pspec_music(x, order, fftsize)
pspec_music(x, order, range)
pspec_music(x, order, 'corr')
pspec_music(x, order, fftsize, fs)
pspec_music(x, order, fftsize, range)
pspec_music(x, order, fftsize, 'corr')
pspec_music(x, order, 'corr', range)
pspec_music(x, order, fftsize, fs, 'corr')
pspec_music(x, order, fftsize, fs, range)
pspec_music(x, order, fftsize, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap)
pspec_music(x, order, fftsize, fs, win, 'corr')
pspec_music(x, order, fftsize, fs, win, range)
pspec_music(x, order, fftsize, fs, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
pspec_music(x, order, fftsize, fs, win, noverlap, range)
pspec_music(x, order, fftsize, fs, win, 'corr', range)
pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
S = pspec_music(x, order)
S = pspec_music(x, order, fftsize)
S = pspec_music(x, order, range)
S = pspec_music(x, order, 'corr')
S = pspec_music(x, order, fftsize, fs)
S = pspec_music(x, order, fftsize, range)
S = pspec_music(x, order, fftsize, 'corr')
S = pspec_music(x, order, 'corr', range)
S = pspec_music(x, order, fftsize, fs, 'corr')
S = pspec_music(x, order, fftsize, fs, range)
S = pspec_music(x, order, fftsize, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap)
S = pspec_music(x, order, fftsize, fs, win, 'corr')
S = pspec_music(x, order, fftsize, fs, win, range)
S = pspec_music(x, order, fftsize, fs, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
S = pspec_music(x, order, fftsize, fs, win, noverlap, range)
S = pspec_music(x, order, fftsize, fs, win, 'corr', range)
S = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w] = pspec_music(x, order)
[S, w] = pspec_music(x, order, fftsize)
[S, w] = pspec_music(x, order, range)
[S, w] = pspec_music(x, order, 'corr')
[S, w] = pspec_music(x, order, fftsize, range)
[S, w] = pspec_music(x, order, fftsize, 'corr')
[S, w] = pspec_music(x, order, 'corr', range)
[S, w] = pspec_music(x, order, fftsize, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs)
[S, f] = pspec_music(x, order, fftsize, fs, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap)
[S, f] = pspec_music(x, order, fftsize, fs, win, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, win, range)
[S, f] = pspec_music(x, order, fftsize, fs, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, range)
[S, f] = pspec_music(x, order, fftsize, fs, win, 'corr', range)
[S, f] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
[S, w, evec, eval] = pspec_music(x, order)
[S, w, evec, eval] = pspec_music(x, order, fftsize)
[S, w, evec, eval] = pspec_music(x, order, range)
[S, w, evec, eval] = pspec_music(x, order, 'corr')
[S, w, evec, eval] = pspec_music(x, order, fftsize, range)
[S, w, evec, eval] = pspec_music(x, order, fftsize, 'corr')
[S, w, evec, eval] = pspec_music(x, order, 'corr', range)
[S, w, evec, eval] = pspec_music(x, order, fftsize, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr')
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, 'corr', range)
[S, f, evec, eval] = pspec_music(x, order, fftsize, fs, win, noverlap, 'corr', range)
Legacy Name: pmusic
Uses the multiple signal classification (MUSIC) algorithm to calculate the pseudospectrum of an input signal. If you do not request an output, this function plots the pseudospectrum in the current plot window.
Name  Description  

x  Specifies the input signal. x can be a real or complex vector or matrix. If you specify 'corr', x is a square autocorrelation matrix. Otherwise, x is a real matrix whose elements are stacked rowwise.  
order  Specifies the dimension information of the signal subspace. order can be a scalar or a vector of two elements. If order is a scalar, order specifies the dimension of the signal subspace. If order is a vector of two elements, order(2) separates the signal and noise subspace. All eigenvalues greater than order(2)*the smallest eigenvalue belong to the signal subspace, and the signal subspace dimension is less than or equal to order(1).  
fftsize  Specifies the length of the FFT to perform on the estimated eigenvectors of the noise subspace. The default is 256.  
fs  Specifies the sampling frequency of the input sequence in Hz.  
win  Specifies the window to apply to each row of the data matrix. win can be an integer or a vector. If win is an integer, LabVIEW applies a Hamming window with a length of win. If win is a vector, win represents the window whose length equals the number of columns in the data matrix. The default is 2*order  1.  
noverlap  Specifies the number of points that overlap when LabVIEW creates the data matrix. The default is 0.  
'corr'  Specifies that x is a square autocorrelation matrix.  
range  Specifies the range of the estimated pseudospectrum. range is a string that accepts the following values.

Name  Description 

S  Returns the estimated pseudospectrum of the input signal. If x is real and range is 'half', the length of S is fftsize / 2 + 1 for an even fftsize and (fftsize + 1) / 2 for an odd fftsize. Otherwise, the length of S is fftsize. 
w  Returns the discrete frequencies in radians per sample at the points where LabVIEW evaluates S. 
f  Returns the frequencies in Hz at the points where LabVIEW evaluates S. 
evec  Returns the matrix whose columns are noise subspace eigenvectors. 
eval  Returns all the eigenvalues of the autocorrelation matrix of the input data. 
The following table lists the support characteristics of this function.
Supported in the LabVIEW RunTime Engine  Yes (if you request output) 
Supported on RT targets  Yes (if you request output) 
Suitable for bounded execution times on RT  Not characterized 
fs = 1000;
t = 0:1/fs:0.2;
fftsize = 1024;
noise = randnormal(size(t));
x = sin(2*pi*100*t) + cos(2*pi*250*t) + noise;
S = pspec_music(x, 4, fftsize, fs);
psd_burg
psd_covar
psd_mcovar
psd_periodogram
psd_welch
psd_yule
pspec_eign