Main Content

Compare the Frequency Content of Two Signals

Spectral coherence helps identify similarity between signals in the frequency domain. Large values indicate frequency components common to the signals.

Load two sound signals into the workspace. They are sampled at 1 kHz. Compute their power spectra using periodogram and plot them next to each other.

load relatedsig

Fs = FsSig;

[P1,f1] = periodogram(sig1,[],[],Fs,'power');
[P2,f2] = periodogram(sig2,[],[],Fs,'power');

subplot(2,1,1)
plot(f1,P1,'k')
grid
ylabel('P_1')
title('Power Spectrum')

subplot(2,1,2)
plot(f2,P2,'r')
grid
ylabel('P_2')
xlabel('Frequency (Hz)')

Figure contains 2 axes. Axes 1 with title Power Spectrum contains an object of type line. Axes 2 contains an object of type line.

Each signal has three frequency components with significant energy. Two of those components appear to be shared. Find the corresponding frequencies using findpeaks.

[pk1,lc1] = findpeaks(P1,'SortStr','descend','NPeaks',3);
P1peakFreqs = f1(lc1)
P1peakFreqs = 3×1

  165.0391
   35.1562
   94.7266

[pk2,lc2] = findpeaks(P2,'SortStr','descend','NPeaks',3);
P2peakFreqs = f2(lc2)
P2peakFreqs = 3×1

  165.0391
   35.1562
  134.7656

The common components are located around 165 and 35 Hz. You can use mscohere to find the matching frequencies directly. Plot the coherence estimate. Find the peaks above a threshold of 0.75.

[Cxy,f] = mscohere(sig1,sig2,[],[],[],Fs);

thresh = 0.75;
[pks,locs] = findpeaks(Cxy,'MinPeakHeight',thresh);
MatchingFreqs = f(locs)
MatchingFreqs = 2×1

   35.1562
  164.0625

figure
plot(f,Cxy)
ax = gca;
grid
xlabel('Frequency (Hz)')
title('Coherence Estimate')
ax.XTick = MatchingFreqs;
ax.YTick = thresh;
axis([0 200 0 1])

Figure contains an axes. The axes with title Coherence Estimate contains an object of type line.

You get the same values as before. You can find the frequency content common to two signals without studying the two signals separately.

See Also

| |

Related Topics