Smoothed Pseudo K-Bertrand time-frequency distribution.
[TFR,T,F] = tfrspbk(X) [TFR,T,F] = tfrspbk(X, T) [TFR,T,F] = tfrspbk(X, T, K) [TFR,T,F] = tfrspbk(X, T, K, NH0) [TFR,T,F] = tfrspbk(X, T, K, NH0,NG0) [TFR,T,F] = tfrspbk(X, T, K, NH0,NG0, FMIN,FMAX) [TFR,T,F] = tfrspbk(X, T, K, NH0,NG0, FMIN,FMAX, N) [TFR,T,F] = tfrspbk(X, T, K, NH0,NG0, FMIN,FMAX, N, TRACE) [TFR,T,F] = tfrspbk(...,'plot')
A Nx elements vector (auto-SPBK) or a Nx by 2 array signal (cross-SPBK).
a real Nt vector : time instant(s) (default: 1:NX).
T must be a uniformly sampled vector whose elements are between 1 and Nx
label of the K-Bertrand distribution. The distribution with parametrization function lambdak(u,K) = (K (exp(-u)-1)/(exp(-Ku)-1))^(1/(K-1)) is computed (default : 0).
K=-1 : Smoothed pseudo (active) Unterberger distribution
K=0 : Smoothed pseudo Bertrand distribution
K=0.5 : Smoothed pseudo D-Flandrin distribution
K=2 : Affine smoothed pseudo Wigner-Ville distribution
half length of the analyzing wavelet at coarsest scale. A Morlet wavelet is used. NH0 controles the frequency smoothing of the smoothed pseudo K-Bertrand distribution.(default : sqrt(Nx)).
half length of the time smoothing window. NG0 = 0 corresponds to the Pseudo K-Bertrand distribution. (default : 0).
a positive scalar in ]0 0.5], the normalized lower frequency bound in (Hz) of the analyzed signal. When unspecified, you have to enter it at the command line from the plot of the spectrum.
a positive scalar in ]0 0.5], the normalized upper frequency bound (in Hz) of the analyzed signal. When unspecified, you have to enter it at the command line from the plot of the spectrum.
positive integer: the number of analyzed voices. When unspecified, you have to enter it at the command line from the plot of the spectrum.
A boolean (or a real scalar) if true (or nonzero),the progression of the algorithm is shown (default : %f).
if one input parameter is 'plot', tfrqview is called and the time-frequency representation will be plotted.
A complex N by Nt array: the time-frequency representation.
abscissa correspond to uniformly sampled time, and ordinates correspond to a geometrically sampled frequency). First row of TFR corresponds to the lowest frequency.
A N vector of normalized frequencies (geometrically sampled from FMIN to FMAX).
tfrspbk generates the auto or cross Smoothed Pseudo K-Bertrand distribution.
N = 64, sig = altes(N,0.1,0.45); [TFR,T,F] = tfrspbk(sig,1:N,0,sqrt(N),0,0.1,0.35,8); clf; gcf().color_map = jetcolormap(128); Sgrayplot(T,F,TFR'); | ![]() | ![]() |