You receive some data and would like to know if it matches a longer stream you have measured. Cross-correlation allows you to make that determination, even when the data are corrupted by noise.
Load into the workspace a recording of a ring spinning on a tabletop. Crop a one-second fragment and listen to it.
load('Ring.mat')
Time = 0:1/Fs:(length(y)-1)/Fs;
m = min(y);
M = max(y);
Full_sig = double(y);
timeA = 7;
timeB = 8;
snip = timeA*Fs:timeB*Fs;
Fragment = Full_sig(snip);
% To hear, type soundsc(Fragment,Fs)
Plot the signal and the fragment. Highlight the fragment endpoints for reference.
plot(Time,Full_sig,[timeA timeB;timeA timeB],[m m;M M],'r--')
xlabel('Time (s)')
ylabel('Clean')
axis tight
plot(snip/Fs,Fragment)
xlabel('Time (s)')
ylabel('Clean')
title('Fragment')
axis tight
Compute and plot the cross-correlation of the full signal and the fragment.
[xCorr,lags] = xcorr(Full_sig,Fragment);
plot(lags/Fs,xCorr)
grid
xlabel('Lags (s)')
ylabel('Clean')
axis tight
The lag at which the cross-correlation is greatest is the time delay between the signals' starting points. Replot the signal and overlay the fragment.
[~,I] = max(abs(xCorr));
maxt = lags(I);
Trial = NaN(size(Full_sig));
Trial(maxt+1:maxt+length(Fragment)) = Fragment;
plot(Time,Full_sig,Time,Trial)
xlabel('Time (s)')
ylabel('Clean')
axis tight
Repeat the procedure, but add noise separately to signal and fragment. The sound cannot be picked out from the noise.
NoiseAmp = 0.2*max(abs(Fragment));
Fragment = Fragment+NoiseAmp*randn(size(Fragment));
Full_sig = Full_sig+NoiseAmp*randn(size(Full_sig));
% To hear, type soundsc(Fragment,Fs)
plot(Time,Full_sig,[timeA timeB;timeA timeB],[m m;M M],'r--')
xlabel('Time (s)')
ylabel('Noisy')
axis tight
The procedure finds the missing fragment despite the high noise level.
[xCorr,lags] = xcorr(Full_sig,Fragment);
plot(lags/Fs,xCorr)
grid
xlabel('Lags (s)')
ylabel('Noisy')
axis tight
[~,I] = max(abs(xCorr));
maxt = lags(I);
Trial = NaN(size(Full_sig));
Trial(maxt+1:maxt+length(Fragment)) = Fragment;
figure
plot(Time,Full_sig,Time,Trial)
xlabel('Time (s)')
ylabel('Noisy')
axis tight
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