Wavelet packet spectrum
[SPEC,TIMES,FREQ]
= wpspectrum(WPT,Fs)
[...] = wpspectrum(WPT,Fs,'plot'
)
[...,TNFO] = wpspectrum(...)
[
returns
a matrix of wavelet packet spectrum estimates, SPEC
,TIMES
,FREQ
]
= wpspectrum(WPT
,Fs
)SPEC
,
for the binary wavelet packet tree object, WPT
. Fs
is
the sampling frequency in Hertz. SPEC
is a 2^{J}byN matrix
where J is the level of the wavelet packet transform
and N is the length of the time series. TIMES
is
a 1byN vector of times and FREQ
is
a 1by2^{J} vector of
frequencies.
[...] = wpspectrum(
displays
the wavelet packet spectrum.WPT
,Fs
,'plot'
)
[...,
returns
the terminal nodes of the wavelet packet tree in frequency order.TNFO
] = wpspectrum(...)



Sampling frequency in Hertz as a scalar of class double. Default: 1 

The character vector 

Wavelet packet spectrum. The frequency spacing between the rows of 

Time vector. 

Frequency vector. 

Terminal nodes. 
Wavelet packet spectrum for signal consisting of two sinusoids with disjoint support:
fs = 500; t = 0:1/fs:4; y = sin(32*pi*t).*(t<2) + sin(128*pi*t).*(t>=2); subplot(2,1,1); plot(t,y); axis tight title('Analyzed Signal'); % Wavelet packet spectrum level = 6; wpt = wpdec(y,level,'sym6'); subplot(2,1,2); [S,T,F] = wpspectrum(wpt,fs,'plot');
Wavelet packet spectrum of chirp:
fs = 1000; t = 0:1/fs:2; % create chirp signal y = sin(256*pi*t.^2); % Plot the analyzed signal subplot(2,1,1); plot(t,y); axis tight title('Analyzed Signal'); % Wavelet packet spectrum level = 6; wpt = wpdec(y,level,'sym8'); subplot(2,1,2); [S,T,F] = wpspectrum(wpt,fs,'plot');
Wickerhauser, M.V. Lectures on Wavelet Packet Algorithms, Technical Report, Washington University, Department of Mathematics, 1992.