This example shows how to obtain spectral information of a signal using continuous wavelet transform analysis. Signal Processing Toolbox™ is needed to run the example.

The tools best suited for a spectral analysis of signals are those based on the FFT. While wavelets are not specifically designed for spectral analysis, we can recover some spectral information using wavelet analysis.

In this example, we perform both Fourier analysis and wavelet analysis of various elementary periodic signals. Then, we compare the spectral information found in each of them.

**Analysis of an Elementary Periodic Signal**

Define a very simple periodic signal, such as a sine signal with frequency of **Frq = 10**.

Fs = 1000; t = 0:1/Fs:1; Frq = 10; x = sin(2*pi*t*Frq); plot(x,'r'); axis tight title('Signal'); xlabel('Time or Space');

Compute the power spectral density (PSD) estimate using spectral estimation of the signal. Then, we find the main frequency by looking at the spectral density and locating the frequency at which the PSD reaches its maximum.

% Plot the spectral density figure pwelch(x,64,32,256,Fs,'onesided'); % Find main frequency [S,F] = pwelch(x,64,32,256,Fs,'onesided'); [~,idxMax] = max(S); FreqMax = F(idxMax) hold on plot([FreqMax,FreqMax],ylim,'m--');

FreqMax = 7.8125

This result is an approximate value of the "true frequency."

Now, we compute the continuous wavelet transform using the **gaus4** wavelet and we will see where to find spectral information.

```
wname = 'gaus4';
scales = 1:1:128;
coefs = cwt(x,scales,wname);
```

Using the function `scal2freq`

, we can compute the correspondence table of scales and frequencies. This table depends on the selected wavelet. We search the scale that corresponds to the frequency **Frq** used to design the signal.

TAB_Sca2Frq = scal2frq(scales,wname,1/Fs); clf plot(TAB_Sca2Frq); axis tight grid hold on plot([scales(1),scales(end)],[Frq Frq],'m--'); ylim([0 100]); title('Correspondence Table of Scales and Frequencies'); xlabel('Scale'); ylabel('Frequency');

We find the scale **Sca** corresponding to the frequency **Frq** for the wavelet **gaus4**.

[~,idxSca] = min(abs(TAB_Sca2Frq-Frq)); Sca = scales(idxSca)

Sca = 50

Now, we use continuous wavelet analysis to compute the scalogram of wavelet coefficients using the wavelet **gaus4**. Plot this scalogram and an horizontal line corresponding to the scale **Sca** associated with the frequency **Frq**. We can see that this line connects the maxima of energy in the scalogram.

wscalogram('image',coefs,'scales',scales,'ydata',x); hold on plot([1 size(coefs,2)],[Sca Sca],'Color','m','LineWidth',2);

In the scalogram, maxima of energy are detected at scale 50, which corresponds to frequency 10. This is one method of using wavelet analysis to obtain spectral information.

We can also use a contour plot to view the spectral information.

clf coefs = cwt(x,scales,wname,'scalCNT'); hold on plot([1 size(coefs,2)],[Sca Sca],'Color','m','LineWidth',2);

The location of frequency information in the scalogram depends on the wavelet used for the analysis. Some wavelets are able to detect the frequency location very well. Now, we will show how other wavelets perform.

Note that the value of the scale **Sca** corresponding to the frequency **Frq** also depends on the wavelet.

*Good detection with Mexican hat wavelet*

```
wname = 'mexh';
TAB_Sca2Frq = scal2frq(scales,wname,1/Fs);
[~,idxSca] = min(abs(TAB_Sca2Frq-Frq));
Sca = scales(idxSca)
```

Sca = 25

clf coefs = cwt(x,scales,wname,'scalCNT'); hold on plot([1 size(coefs,2)],[Sca Sca],'Color','m','LineWidth',2);

*Good detection with Morlet wavelet*

```
wname = 'morl';
TAB_Sca2Frq = scal2frq(scales,wname,1/Fs);
[~,idxSca] = min(abs(TAB_Sca2Frq-Frq));
Sca = scales(idxSca)
```

Sca = 81

clf coefs = cwt(x,scales,wname,'scalCNT'); hold on plot([1 size(coefs,2)],[Sca Sca],'Color','m','LineWidth',2);

*Poor detection with Haar wavelet*

```
wname = 'haar';
TAB_Sca2Frq = scal2frq(scales,wname,1/Fs);
[~,idxSca] = min(abs(TAB_Sca2Frq-Frq));
Sca = scales(idxSca)
```

Sca = 100

**Analysis of a Sum of Two Periodic Signals**

We now define a more complicated periodic signal, which is a sum of two sines of frequencies **F1 = 10** and **F2 = 40**. We will compute the power spectral density (PSD) estimate using spectral estimation and the continuous wavelet transform using the wavelet **gaus4**.

First, we build and plot the analyzed signal.

F1 = 10; F2 = 40; Fs = 1000; t = 0:1/Fs:1; x = sin(2*pi*t*F1) + sin(2*pi*t*F2); clf plot(x,'r'); axis tight title('Signal'); xlabel('Time or Space');

Then, compute and plot the PSD of the signal.

```
figure
pwelch(x,64,32,256,Fs,'onesided');
```

Using an interpolated function of the PSD function and locating the two first local maxima of this function, we see approximations of the two "true" frequencies at F1_approx = 7.8 and F2_approx = 39.1.

Using the function `scal2freq`

, we compute the correspondence table of scales and frequencies for the **gaus4** wavelet. Then, we find the scales corresponding to the frequencies **F1 = 10** and **F2 = 40**.

```
wname = 'gaus4';
scales = 1:1:128;
TAB_Sca2Frq = scal2frq(scales,wname,1/Fs);
[~,idxSca_1] = min(abs(TAB_Sca2Frq-F1));
Sca_1 = scales(idxSca_1)
[~,idxSca_2] = min(abs(TAB_Sca2Frq-F2));
Sca_2 = scales(idxSca_2)
```

Sca_1 = 50 Sca_2 = 13

Now, we compute the continuous wavelet transform of the signal, and then, plot the scalogram of wavelet coefficients and two horizontal lines corresponding to the scales **Sca_1** and **Sca_2** linked to the frequencies **F1** and **F2**. We can see that these lines are associated to the local maxima of energy in the scalogram.

Note that the component with the lowest frequency contains the most energy.

clf coefs = cwt(x,scales,wname,'scalCNT'); hold on plot([1 size(coefs,2)],[Sca_1 Sca_1],'Color','m','LineWidth',2); plot([1 size(coefs,2)],[Sca_2 Sca_2],'Color','m','LineWidth',1);

As above, we can use an image representation instead of a contour plot.

clf wscalogram('image',coefs,'scales',scales,'ydata',x); hold on plot([1 size(coefs,2)],[Sca_1 Sca_1],'Color','m','LineWidth',2); plot([1 size(coefs,2)],[Sca_2 Sca_2],'Color','w','LineWidth',1);

**Analysis of a Periodic Signal Corrupted by a Noise**

We define a periodic signal which is a sum of two sines of frequencies **F1 = 10** and **F2 = 40** corrupted by normally distributed white noise. We will compute the power spectral density (PSD) estimate using spectral estimation and the continuous wavelet transform using the wavelet **gaus4**.

We build the analyzed signal.

F1 = 10; F2 = 40; Fs = 1000; t = 0:1/Fs:1; x = sin(2*pi*t*F1) + sin(2*pi*t*F2); wn = randn(1,length(x)); wn = 1.5*wn/std(wn); xn = x + wn;

We compute and plot the PSD of the signal.

```
figure
pwelch(x,64,32,256,Fs,'onesided');
```

We still see the two main frequencies in the spectrogram.

We now compute the correspondence table of values of scales and frequencies for the **gaus4** wavelet. Then, we find the scales corresponding to the frequencies **F1 = 10** and **F2 = 40**.

```
wname = 'gaus4';
scales = 1:1:128;
TAB_Sca2Frq = scal2frq(scales,wname,1/Fs);
[~,idxSca_1] = min(abs(TAB_Sca2Frq-F1));
Sca_1 = scales(idxSca_1)
[mini,idxSca_2] = min(abs(TAB_Sca2Frq-F2));
Sca_2 = scales(idxSca_2)
```

Sca_1 = 50 Sca_2 = 13

Next, we compute the continuous wavelet transform of the signal, and then, plot the scalogram of wavelet coefficients and two horizontal lines corresponding to the scales **Sca_1** and **Sca_2** linked to the frequencies **F1** and **F2**.

coefs = cwt(xn,scales,wname); clf wscalogram('image',coefs,'scales',scales,'ydata',xn); hold on plot([1 size(coefs,2)],[Sca_1 Sca_1],'Color','m','LineWidth',2); plot([1 size(coefs,2)],[Sca_2 Sca_2],'Color','w','LineWidth',1);

Although less clear than in the noiseless case, we can see that these lines are still associated with the local maxima of energy in the scalogram.

**Analysis of a More Complicated Signal**

Now we design a more complicated signal, which is a piecewise sine defined on three adjacent intervals: frequency **F1 = 10** for the intervals [0 0.25] and [0.75 1] which correspond to the indices 1:250 and 750:1000, and frequency **F2 = 40** for the interval [0.25 0.75] which corresponds to the indices 251:749.

We build the analyzed signal,

F1 = 10; F2 = 40; Fs = 1000; t = 0:1/Fs:1; x = sin(2*pi*t*F1).*((t<0.25)+(t>0.75)) + sin(2*pi*t*F2).*(t>0.25).*(t<0.75);

and display the analyzed signal.

clf plot(x); axis tight title('Signal'); xlabel('Time or Space');

Now we compute and plot the PSD of the signal.

```
figure
pwelch(x,64,32,256,Fs,'onesided');
```

The spectrum is very similar to that achieved for the case of the sum of two sines. It gives no information on the location of any events in time or space.

Now, compute the continuous wavelet transform of the signal and plot the scalogram of wavelet coefficients and two horizontal lines corresponding to the scales **Sca_1** and **Sca_2** linked to the frequencies **F1** and **F2**. We will also draw the two vertical lines that separate the intervals.

wname = 'gaus4'; scales = 1:1:128; coefs = cwt(x,scales,wname); clf wscalogram('image',coefs,'scales',scales,'ydata',x); hold on plot([1 size(coefs,2)],[Sca_1 Sca_1],'Color','m'); plot([1 size(coefs,2)],[Sca_2 Sca_2],'Color','m'); plot([250 250],[1 128],'Color','g','LineWidth',2); plot([750 750],[1 128],'Color','g','LineWidth',2);

Note that the wavelet analysis works very effectively to detect time or space events. The intervals with the different frequencies are clearly detected.

**Summary**

In general, the tools best suited for spectral analysis of signals are based on FFT, however, wavelets, though not specifically dedicated to this type of analysis, can recover some of this spectral information.

The wavelet procedure we used was to use both the continuous wavelet transform and the function `scal2freq`

to compute a correspondence table between values of scales and frequencies. This table depends on the selected wavelet. Then, we searched the scale corresponding to the frequency or vice versa.

This procedure produces approximated values, however they are generally good enough when applied to signals that are not too complex. This procedure is also very efficient at detecting time or space events.

Was this topic helpful?