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Magnitude-squared coherence


Cxy = mscohere(x,y)
Cxy = mscohere(x,y,window)
Cxy = mscohere(x,y,window,noverlap)
[Cxy,W] = mscohere(x,y,window,noverlap,nfft)
[Cxy,F] = mscohere(x,y,window,noverlap,nfft,fs)
[Cxy,F] = mscohere(x,y,window,noverlap,f,fs)
[...] = mscohere(x,y,...,'twosided')


Cxy = mscohere(x,y) finds the magnitude-squared coherence estimate, Cxy, of the input signals, x and y, using Welch's averaged modified periodogram method.

The input signals may be either vectors or two-dimensional matrices. If both are vectors, they must have the same length. If both are matrices, they must have the same size, and mscohere operates columnwise: Cxy(:,n) = mscohere(x(:,n),y(:,n)). If one is a matrix and the other is a vector, then the vector is converted to a column vector and internally expanded so both inputs have the same number of columns.

For real x and y, mscohere returns a one-sided coherence estimate. For complex x or y, it returns a two-sided estimate.

mscohere uses the following default values:

ParameterDescriptionDefault Value


FFT length which determines the frequencies at which the coherence is estimated

For real x and y, the length of Cxy is (nfft/2 + 1) if nfft is even or (nfft + 1)/2 if nfft is odd. For complex x or y, the length of Cxy is nfft.

If nfft is greater than the signal length, the data is zero-padded. If nfft is less than the signal length, the data segment is wrapped so that the length is equal to nfft.

Maximum of 256 or the next power of 2 greater than the length of each section of x or y


Sampling frequency



Windowing function and number of samples to use for each section

Periodic Hamming window of sufficient length to obtain eight equal sections of x and y


Number of samples by which the sections overlap

Value to obtain 50% overlap

    Note   You can use the empty matrix, [], to specify the default value for any input argument except x or y. For example, Pxy = mscohere(x,y,[],[],128) uses a Hamming window, default noverlap to obtain 50% overlap, and the specified 128 nfft.

Cxy = mscohere(x,y,window) specifies a windowing function, divides x and y into equal overlapping sections of the specified window length, and windows each section using the specified window function. If you supply a scalar for window, then Cxy uses a Hamming window of that length.

Cxy = mscohere(x,y,window,noverlap) overlaps the sections of x by noverlap samples. noverlap must be an integer smaller than the length of window.

[Cxy,W] = mscohere(x,y,window,noverlap,nfft) uses the specified FFT length nfft to calculate the coherence estimate. It also returns W, which is the vector of normalized frequencies (in rad/sample) at which the coherence is estimated. For real x and y, Cxy length is (nfft/2 + 1) if nfft is even; if nfft is odd, the length is (nfft + 1)/2. For complex x or y, the length of Cxy is nfft. For real signals, the range of W is [0, π] when nfft is even and [0, π) when nfft is odd. For complex signals, the range of W is [0, 2π).

[Cxy,F] = mscohere(x,y,window,noverlap,nfft,fs) returns Cxy as a function of frequency and a vector F of frequencies at which the coherence is estimated. fs is the sampling frequency in Hz. For real signals, the range of F is [0, fs/2] when nfft is even and [0, fs/2) when nfft is odd. For complex signals, the range of F is [0, fs).

[Cxy,F] = mscohere(x,y,window,noverlap,f,fs) computes the coherence estimate at the frequencies f. f is a vector containing two or more elements.

[...] = mscohere(x,y,...,'twosided') returns a coherence estimate with frequencies that range over the whole Nyquist interval. Specifying 'onesided' uses half the Nyquist interval.

mscohere(...) plots the magnitude-squared coherence versus frequency in the current figure window.

    Note   If you estimate the magnitude-squared coherence with a single window, or section, the value is identically 1 for all frequencies [1]. You must use at least two sections.


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Compute and plot the coherence estimate between two colored noise sequences, x and y. Reset the random number generator for reproducible results. Use a Hann window to estimate the coherence.

rng default
r = randn(16384,1);

h1 = ones(1,10)/sqrt(10);
x = filter(h1,1,r);

h = fir1(30,0.2,rectwin(31));
y = filter(h,1,x);


Generate two sinusoidal signals sampled for 1 second each at 1 kHz. Each sinusoid has a frequency of 250 Hz. One of the signals lags the other in phase by π/3 radians. Embed both signals in white Gaussian noise of unit variance. Reset the random number generator for reproducible results.

rng default
fs = 1000;
f = 250;
t = 0:1/fs:1-1/fs;
um = sin(2*pi*f*t)+rand(size(t));
un = sin(2*pi*f*t-pi/3)+rand(size(t));

Use mscohere to compute and plot the magnitude-squared coherence of the signals.


Modify the title of the plot, the label of the x-axis, and the limits of the y-axis.

title('Magnitude-Squared Coherence')
xlabel('f (Hz)')
ylim([0 1.1])

Use gca to obtain a handle to the current axes. Change the locations of the tick marks. Remove the label of the y-axis.

ax = gca;
ax.XTick = 0:250:500;
ax.YTick = 0:0.25:1;
ax.YLabel.String = [];

Invoke the Children property of the handle to change the color and width of the plotted line.

ln = ax.Children;
ln.Color = [0.8 0 0];
ln.LineWidth = 1.5;

Alternatively, use set and get to modify the line properties.

set(get(gca,'Children'),'Color',[0 0.4 0],'LineStyle','--','LineWidth',1)

Related Examples

More About

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Magnitude-Squared Coherence

The magnitude-squared coherence estimate is a function of frequency with values between 0 and 1 that indicates how well x corresponds to y at each frequency. The magnitude-squared coherence is a function of the power spectral densities, Pxx(f) and Pyy(f), of x and y, and the cross power spectral density, Pxy(f), of x and y:



mscohere estimates the magnitude-squared coherence function [2] using Welch's overlapped averaged periodogram method (see references [3] and [4]).


[1] Stoica, Petre, and Randolph Moses. Spectral Analysis of Signals. Upper Saddle River, NJ: Prentice Hall, 2005.

[2] Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.

[3] Rabiner, Lawrence R., and Bernard Gold. Theory and Application of Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.

[4] Welch, Peter D. "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodograms." IEEE® Transactions on Audio and Electroacoustics. Vol. AU-15, 1967, pp. 70–73.

Introduced before R2006a

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