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Scale to frequency


F = scal2frq(A,'wname',DELTA)


F = scal2frq(A,'wname',DELTA) returns the pseudo-frequencies corresponding to the scales given by A and the wavelet function 'wname' (see wavefun for more information) and the sampling period DELTA.

scal2frq(A,'wname') is equivalent to scal2frq(A,'wname',1).

There is only an approximate answer for the relationship between scale and frequency.

In wavelet analysis, the way to relate scale to frequency is to determine the center frequency of the wavelet, Fc, and use the following relationship:



  • a is a scale.

  • Fc is the center frequency of the wavelet in Hz.

  • Fa is the pseudo-frequency corresponding to the scale a, in Hz.

The idea is to associate with a given wavelet a purely periodic signal of frequency Fc. The frequency maximizing the Fourier transform of the wavelet modulus is Fc. centfrq computes the center frequency for a specified wavelet. From the above relationship, it can be seen that scale is inversely proportional to pseudo-frequency. For example, if the scale increases, the wavelet becomes more spread out, resulting in a lower pseudo-frequency.

Some examples of the correspondence between the center frequency and the wavelet are shown in the following figure.

Center Frequencies for Real and Complex Wavelets

As you can see, the center frequency-based approximation captures the main wavelet oscillations. The center frequency is a convenient and simple characterization of the dominant frequency of the wavelet.


collapse all

Construct a vector of scales with 10 voices per octave over five octaves. Assume the data are sampled at 10 kHz.

voicesperoctave = 10;
numoctaves = 5;
a0 = 2^(1/voicesperoctave);
Fs = 1e4;
scales = ...

Convert the scales to approximate frequencies in hertz for the Morlet wavelet.

Frq = scal2frq(scales,'morl',1/Fs);

Determine the corresponding periods. Construct a table with the scales, the corresponding frequencies, and periods. Display the smallest 20 scales along with their corresponding frequencies and periods.

Frq = Frq(:);
scales = scales(:);
T = [scales.*(1/Fs) Frq 1./Frq];
T = array2table(T,'VariableNames',{'Scale','Frequency','Period'});
ans =

  20x3 table

      Scale       Frequency      Period  
    __________    _________    __________

        0.0002    4062.5       0.00024615
    0.00020139    4034.4       0.00024787
    0.00020279    4006.6       0.00024959
     0.0002042    3978.9       0.00025133
    0.00020562    3951.4       0.00025307
    0.00020705    3924.1       0.00025483
    0.00020849      3897       0.00025661
    0.00020994    3870.1       0.00025839
     0.0002114    3843.4       0.00026019
    0.00021287    3816.8         0.000262
    0.00021435    3790.4       0.00026382
    0.00021585    3764.3       0.00026566
    0.00021735    3738.3        0.0002675
    0.00021886    3712.4       0.00026936
    0.00022038    3686.8       0.00027124
    0.00022191    3661.3       0.00027312
    0.00022346      3636       0.00027502
    0.00022501    3610.9       0.00027694
    0.00022658      3586       0.00027886
    0.00022815    3561.2        0.0002808

The example shows how to create a contour plot of the CWT using approximate frequencies in Hz.

Create a signal consisting of two sine waves with disjoint support in additive noise. Assume the signal is sampled at 1 kHz.

Fs = 1000;
t = 0:1/Fs:1-1/Fs;
x = 1.5*cos(2*pi*100*t).*(t<0.25)+1.5*cos(2*pi*50*t).*(t>0.5 & t<=0.75);
x = x+0.05*randn(size(t));

Obtain the CWT of the input signal and plot the result.

[cfs,f] = cwt(x,Fs);
axis tight;
grid on;
ylabel('Approximate Frequency (Hz)');
title('CWT with Time vs Frequency');


Abry, P. (1997), Ondelettes et turbulence. Multirésolutions, algorithmes de décomposition, invariance d'échelles, Diderot Editeur, Paris.

See Also

Introduced before R2006a

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