Smoothed Pseudo Wigner-Ville time-frequency distribution.
[TFR,T,F] = tfrspwv(X) [TFR,T,F] = tfrspwv(X, T) [TFR,T,F] = tfrspwv(X, T, N) [TFR,T,F] = tfrspwv(X, T, N, G [TFR,T,F] = tfrspwv(X, T, N, G, H) [TFR,T,F] = tfrspwv(X, T, N, G, H, TRACE) [TFR,T,F] = tfrspwv(...,'plot')
A Nx elements vector (auto-SPWV) or a Nx by 2 array signal (cross-SPWV).
a real Nt vector with elements in [1 Nx] : time instant(s) (default: 1:NX).
a positive integer: the number of frequency bins (default:NX). For faster computation N should be a power of 2.
a real vector with odd length: the time smoothing window, (default :Hamming(N/10)).
It will be normalized such as the middle point equals 1 to preserve signal energy .
real vector with odd length: the frequency smoothing window,(default: Hamming(N/4)).
It will be normalized such as the middle point equals 1 to preserve signal energy.
if nonzero,the progression of the algorithm is shown (default : 0).
if the last input parameter value is 'plot', tfrqview is called and the time-frequency representation will be plotted.
A real N by Nt array: the time-frequency representation.
A N vector of normalized frequencies.
computes the Smoothed Pseudo Wigner-Ville distribution of a discrete-time signal X, or the cross Smoothed Pseudo Wigner-Ville representation between two signals.
N = 128; sig = fmlin(N,0.05,0.15) + fmlin(N,0.3,0.4); g = window("kr",15,3*%pi); h = window("kr",63,3*%pi); [TFR,T,F] = tfrspwv(sig,1:N,N/2,g,h); clf; gcf().color_map = jetcolormap(128); Sgrayplot(T,F,TFR'); | ![]() | ![]() |