## Adding a Controlled Amount of Noise to a Noise-Free Signal

January 3, 201221 comments Coded in Matlab

The following gives two snippets of Matlab code. The first snippet is a function whose inputs are: (1) a real-valued signal, and (2) a desired signal-to-noise ratio (SNR) measured in dB. That 'SNR_Set()' function returns the real-valued input 'Signal' contaminated with normally-distributed, zero-mean, random noise.  The signal-to-noise ratio (SNR in dB) of the output 'Noisy_Signal' signal is controlled by the input 'Desired_SNR_dB' argument measured in dB.

The second snippet of code is a short routine that tests the 'SNR_Set()' function code. Be aware that you're dealing with random signals. So a single output signal's SNR will not be exactly equal to the desired SNR. But if you run the code 100 times, the 'average' of the SNRs of your results will be quite close to your desired SNR.

function [Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB)
%
%   SNR_Set(x, Desired_SNR_dB) returns the real-valued
%   input 'Signal' contaminated with normally-distributed,
%   zero-mean, random noise.  The signal-to-noise ratio
%   (SNR in dB) of the output 'Noisy_Signal' signal is
%   controlled by the input 'Desired_SNR_dB' argument measured
%   in dB.

%   Example:
% 	Npts = 128;                  % Number of time samples
% 	n = 0:Npts-1;                % Time-domain index
% 	Signal = 3*sin(2*pi*3*n/Npts); % Real-valued signal
% 	Desired_SNR_dB = 3;      % Set SNR of output 'Noisy_Signal' to +3 dB
% 	[Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB);
%
%   Author: Richard Lyons [December 2011]
%******************************************

Npts = length(Signal); % Number of input time samples
Noise = randn(1,Npts); % Generate initial noise; mean zero, variance one

Signal_Power = sum(abs(Signal).*abs(Signal))/Npts;
Noise_Power = sum(abs(Noise).*abs(Noise))/Npts;
%Initial_SNR = 10*(log10(Signal_Power/Noise_Power));

K = (Signal_Power/Noise_Power)*10^(-Desired_SNR_dB/10);  % Scale factor

New_Noise = sqrt(K)*Noise; % Change Noise level
%New_Noise_Power = sum(abs(New_Noise).*abs(New_Noise))/Npts
%New_SNR = 10*(log10(Signal_Power/New_Noise_Power))

Noisy_Signal = Signal + New_Noise;

'SNR_Set()' Function Test Code:
%  Filename SNR_Set_test.m
%
%  Tests the 'SNR_Set()" function.  Adds a predefined
%  amount of random noise to a noise-free signal such that
%  the noisy signal has a desired signal-to-noise ratio (SNR).
%
%  Author: Richard Lyons [December 2011]

clear, clc

% Create a noise-free signal
Npts = 128; % Number of time samples
n = 0:Npts-1; % Time-domain index
Cycles = 5;  % Integer number of cycles in noise-free sinwave signal
Signal = 3*sin(2*pi*Cycles*n/Npts); % Real-valued noise-free signal

Desired_SNR_dB = 3 % Set desired SNR in dB

[Noisy_Signal] = SNR_Set(Signal, Desired_SNR_dB); % Generate noisy signal

% Plot original and 'noisy' signals
figure(1)
subplot(2,1,1)
plot(n, Signal, '-bo', 'markersize', 2)
title('Original Signal')
grid on, zoom on
subplot(2,1,2)
plot(n, Noisy_Signal, '-bo', 'markersize', 2)
title('Noisy Signal')
xlabel('Time-samples')
grid on, zoom on

% Measure SNR in freq domain
Spec = fft(Noisy_Signal);
Spec_Mag = abs(Spec); % Spectral magnitude
figure(2)
plot(Spec_Mag, '-bo', 'markersize', 2)
title('Spec Mag of Noisy Signal')
xlabel('Freq-samples'), ylabel('Linear')
grid on, zoom on
Signal_Power = Spec_Mag(Cycles+1)^2 + Spec_Mag(Npts-Cycles+1)^2;
Noise_Power = sum(Spec_Mag.^2) -Signal_Power;
Measured_SNR = 10*log10(Signal_Power/Noise_Power)