snr

Signal-to-noise ratio

Syntax

  • r = snr(pxx,f,'psd') example
  • r = snr(pxx,f,n,'psd')
  • r = snr(sxx,f,rbw,'power') example
  • r = snr(sxx,f,rbw,n,'power')

Description

example

r = snr(x,y) returns the signal-to-noise ratio (SNR) in decibels of a signal, x, by computing the ratio of its summed squared magnitude to that of the noise, y. y must have the same dimensions as x. Use this form when the input signal is not necessarily sinusoidal and you have an estimate of the noise.

example

r = snr(x) returns the SNR in decibels relative to the carrier (dBc) of a real-valued sinusoidal input signal, x. The SNR is determined using a modified periodogram of the same length as the input. The modified periodogram uses a Kaiser window with β = 38. The result excludes the power of the first six harmonics, including the fundamental.

example

r = snr(x,fs,n) returns the SNR in dBc of a real sinusoidal input signal, x, sampled at a rate fs. The computation excludes the power contained in the lowest n harmonics, including the fundamental. The default value of fs is 1. The default value of n is 6.

example

r = snr(pxx,f,'psd') specifies the input pxx as a one-sided power spectral density (PSD) estimate. The argument f is a vector of the frequencies at which the estimates of pxx occur. The computation of noise excludes the power of the first six harmonics, including the fundamental.

r = snr(pxx,f,n,'psd') specifies the number of harmonics, n, to exclude when computing the SNR. The default value of n is 6 and includes the fundamental.

example

r = snr(sxx,f,rbw,'power') specifies the input as a one-sided power spectrum, sxx, of a real signal. The input rbw is the resolution bandwidth over which each power estimate is integrated.

r = snr(sxx,f,rbw,n,'power') specifies the number of harmonics, n, to exclude when computing the SNR. The default value of n is 6 and includes the fundamental.

example

[r,noisepow] = snr(___) also returns the total noise power of the nonharmonic components of the signal.

example

snr(___) with no output arguments plots the spectrum of the signal in the current figure window and labels its main features. It uses different colors to draw the fundamental component, the DC value and the harmonics, and the noise. The SNR appears above the plot. This functionality works for all syntaxes listed above except snr(x,y).

Examples

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Signal-to-Noise Ratio for Rectangular Pulse with Gaussian Noise

Compute the signal-to-noise ratio (SNR) of a 20 ms rectangular pulse sampled for 2 s at 10 kHz in the presence of Gaussian noise. Set the random number generator to the default settings for reproducible results.

rng default
Tpulse = 20e-3;
Fs = 10e3;
t = -1:1/Fs:1;
x = rectpuls(t,Tpulse);
y = 0.00001*randn(size(x));
s = x + y;
pulseSNR = snr(x,s-x)
pulseSNR =
   80.0818

Compare SNR with THD and SINAD

Compute and compare the signal-to-noise ratio (SNR), the total harmonic distortion (THD), and the signal to noise and distortion ratio (SINAD) of a signal.

Create a sinusoidal signal sampled at 48 kHz. The signal has a fundamental of frequency 1 kHz and unit amplitude. It additionally contains a 2 kHz harmonic with half the amplitude and additive noise with variance 0.12. Set the random number generator to the default settings for reproducible results.

rng default
fs = 48e3;
t = 0:1/fs:1-1/fs;
A = 1.0; powfund = A^2/2;
a = 0.4; powharm = a^2/2;
s = 0.1; varnoise = s^2;
x = A*cos(2*pi*1000*t) + ...
    a*sin(2*pi*2000*t) + s*randn(size(t));

Verify that SNR, THD, and SINAD agree with their definitions.

SNR = snr(x);
defSNR = 10*log10(powfund/varnoise);
SN = [SNR defSNR]

THD = thd(x);
defTHD = 10*log10(powharm/powfund);
TH = [THD defTHD]

SINAD = sinad(x);
defSINAD = 10*log10(powfund/(powharm+varnoise));
SI = [SINAD defSINAD]
SN =
   17.0178   16.9897
TH =
   -7.9546   -7.9588
SI =
    7.4571    7.4473

Noise Power

Compute the noise power in the sinusoid from the preceding example. Verify that it agrees with the definition. Set the random number generator to the default settings for reproducible results.

rng default
fs = 48e3;
t = 0:1/fs:1-1/fs;
A = 1.0; powfund = A^2/2;
a = 0.4; powharm = a^2/2;
s = 0.1; varnoise = s^2;
x = A*cos(2*pi*1000*t) + ...
    a*sin(2*pi*2000*t) + s*randn(size(t));
[SNR npow]=snr(x,fs);
[10*log10(powfund)-npow SNR]
ans =
   17.0281   17.0178

Signal-to-Noise Ratio of a Sinusoid

Compute the SNR of a 2.5 kHz sinusoid sampled at 48 kHz. Add white noise with standard deviation 0.001. Set the random number generator to the default settings for reproducible results.

rng default
Fi = 2500; Fs = 48e3; N = 1024;
x = sin(2*pi*Fi/Fs*(1:N)) + 0.001*randn(1,N);
SNR = snr(x,Fs)
SNR =
   57.7103

SNR of a Sinusoid Using the PSD

Obtain the periodogram power spectral density (PSD) estimate of a 2.5 kHz sinusoid sampled at 48 kHz. Add white noise with standard deviation 0.00001. Use this value as input to determine the SNR. Set the random number generator to the default settings for reproducible results.

rng default
Fi = 2500; Fs = 48e3; N = 1024;
x = sin(2*pi*Fi/Fs*(1:N)) + 0.00001*randn(1,N);
w = kaiser(numel(x),38);
[Pxx, F] = periodogram(x,w,numel(x),Fs);
SNR = snr(Pxx,F,'psd')
SNR =
   97.7446

SNR of a Sinusoid Using the Power Spectrum

Compute the SNR of the sinusoid from the preceding example, using the power spectrum. Set the random number generator to the default settings for reproducible results.

rng default
Fi = 2500; Fs = 48e3; N = 1024;
x = sin(2*pi*Fi/Fs*(1:N)) + 0.00001*randn(1,N);
w = kaiser(numel(x),38);
[Sxx, F] = periodogram(x,w,numel(x),Fs,'power');
rbw = enbw(w,Fs);
SNR = snr(Sxx,F,rbw,'power')
SNR =
   97.7446

SNR of an Amplified Signal

Generate a sinusoid of frequency 2.5 kHz sampled at 50 kHz. Reset the random number generator. Add Gaussian white noise with standard deviation 0.00005 to the signal. Pass the result through a weakly nonlinear amplifier. Plot the SNR.

rng default
fs = 5e4; f0 = 2.5e3;
N = 1024; t = (0:N-1)/fs;

ct = cos(2*pi*f0*t);
cd = ct + 0.00005*randn(size(ct));

amp = [1e-5 5e-6 -1e-3 6e-5 1 25e-3];
sgn = polyval(amp,cd);

snr(sgn,fs);

The DC component and all harmonics, including the fundamental, are excluded from the noise measurement. The fundamental and harmonics are labeled.

Input Arguments

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x — Real-valued input signalreal vector

Real-valued input signal, specified as a row or column vector.

Data Types: double | single

y — Noise estimatereal vector

Estimate of the noise in the input signal, specified as a real-valued row or column vector. It must have the same dimensions as x.

Data Types: double | single

fs — Sampling frequency1 (default) | positive real scalar

Sampling frequency, specified as a positive scalar. The sampling frequency is the number of samples per unit time. If the unit of time is seconds, the sampling frequency has units of hertz.

Data Types: double | single

n — Number of harmonics6 (default) | positive integer scalar

Number of harmonics to exclude from the SNR computation, specified as a positive integer scalar. The default value of n is 6.

pxx — One-sided PSD estimatevector

One-sided power spectral density estimate, specified as a real-valued, nonnegative column vector.

Data Types: double | single

f — Cyclical frequenciesreal-valued row or column vector

Cyclical frequencies of the one-sided PSD estimate, pxx, specified as a row or column vector. The first element of f must be 0.

Data Types: double | single

sxx — Power spectrumnonnegative real-valued row or column vector

Power spectrum, specified as a real-valued nonnegative row or column vector.

Data Types: double | single

rbw — Resolution bandwidthpositive scalar

Resolution bandwidth, specified as a positive scalar. The resolution bandwidth is the product of the frequency resolution of the discrete Fourier transform and the equivalent noise bandwidth of the window.

Data Types: double | single

Output Arguments

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r — Signal-to-noise ratioreal-valued scalar

Signal-to-noise ratio, expressed in decibels relative to the carrier (dBc), returned as a real-valued scalar. The SNR is returned in decibels (dB) if the input signal is not sinusoidal.

Data Types: double | single

noisepow — Total noise powerreal-valued scalar

Total noise power of the nonharmonic components of the input signal, returned as a real-valued scalar.

Data Types: double | single

More About

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Distortion Measurement Functions

The functions thd, sfdr, sinad, and snr measure the response of a weakly nonlinear system stimulated by a sinusoid.

When given time-domain input, snr performs a periodogram using a Kaiser window with large sidelobe attenuation. To find the fundamental frequency, the algorithm searches the periodogram for the largest nonzero spectral component. It then computes the central moment of all adjacent bins that decrease monotonically away from the maximum. To be detectable, the fundamental should be at least in the second frequency bin. Higher harmonics are at integer multiples of the fundamental frequency. If a harmonic lies within the monotonically decreasing region in the neighborhood of another, its power is considered to belong to the larger harmonic. This larger harmonic may or may not be the fundamental.

The function estimates a noise level using the median power in the regions containing only noise. The DC component is excluded from the calculation. The noise at each point is the estimated level or the ordinate of the point, whichever is smaller. The noise is then subtracted from the values of the signal and the harmonics.

snr fails if the fundamental is not the highest spectral component in the signal.

Ensure that the frequency components are far enough apart to accommodate for the sidelobe width of the Kaiser window. If this is not feasible, you can use the 'power' flag and compute a periodogram with a different window.

See Also

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