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tfrpmh

Pseudo Margenau-Hill time-frequency distribution

Calling Sequence

[TFR,T,F] = tfrpmh(X)
[TFR,T,F] = tfrpmh(X, T)
[TFR,T,F] = tfrpmh(X, T, N)
[TFR,T,F] = tfrpmh(X, T, N, H)
[TFR,T,F] = tfrpmh(X, T, N, H, TRACE)
[TFR,T,F] = tfrpmh(...,'plot')

Parameters

X :

A Nx elements vector (auto-PMH) or a Nx by 2 array signal (cross-PMH).

T:

a real Nt vector with elements in [1 Nx] : time instant(s) (default: 1:NX).

N:

a positive integer: the number of frequency bins (default:Nx). For faster computation N should be a power of 2.

H :

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.

TRACE :

A boolean (or a real scalar) if true (or nonzero),the progression of the algorithm is shown (default : %f).

'plot':

if one input parameter is 'plot', tfrqview is called and the time-frequency representation will be plotted.

TFR :

A real N by Nt array: the time-frequency representation.

F :

A N vector of normalized frequencies.

Description

tfrcw computes the Choi-Williams distribution of a discrete-time signal X, or the cross Choi-Williams representation between two signals.

Description

tfrpmh computes the Pseudo Margenau-Hill distribution of a discrete-time signal X, or the cross Pseudo Margenau-Hill representation between two signals.

Examples

Interactive use

N = 128;
sig = fmlin(N,0.1,0.4);
h = window("kr",N/2-1,3*%pi); 
tfrpmh(sig,1:N,N,h,'plot');

Non interactive use

N = 128;
sig = fmlin(N,0.1,0.4);
h = window("kr",N/2-1,3*%pi); 
[TFR,T,F] = tfrpmh(sig,1:N,N,h);
clf; gcf().color_map = jetcolormap(128);
grayplot(T,F,TFR');

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