matlab 带限噪声,Matlab信号上叠加噪声和信噪比的计算
function [Y,NOISE] = noisegen(X,SNR)
% noisegen add white Gaussian noise to a signal.
% [Y, NOISE] = NOISEGEN(X,SNR) adds white Gaussian NOISE to X. The SNR is in dB.
NOISE=randn(size(X));
NOISE=NOISE-mean(NOISE);
signal_power = 1/length(X)*sum(X.*X);
noise_variance = signal_power / ( 10^(SNR/10) );
NOISE=sqrt(noise_variance)/std(NOISE)*NOISE;
Y=X+NOISE;
其中X是纯信号，SNR是要求的信噪比，Y是带噪信号，NOISE是叠加在信号上的噪声。 2，把指定的噪声叠加到信号上去 有标准噪声库NOISEX-92，其中带有白噪声、办公室噪声、工厂噪声、汽车噪声、坦克噪声等等，在信号处理中往往需要把库中的噪声叠加到信号中去，而噪声的采样频率与纯信号的采样频率往往不一致，需要采样频率的校准。
function [Y,NOISE] = add_noisem(X,filepath_name,SNR,fs)
% add_noisem add determinated noise to a signal.
% X is signal, and its sample frequency is fs;
% filepath_name is NOISE's path and name, and the SNR is signal to noise ratio in dB.
[wavin,fs1,nbits]=wavread(filepath_name);
if fs1~=fs
wavin1=resample(wavin,fs,fs1);
end
nx=size(X,1);
NOISE=wavin1(1:nx);
NOISE=NOISE-mean(NOISE);
signal_power = 1/nx*sum(X.*X);
noise_variance = signal_power / ( 10^(SNR/10) );
NOISE=sqrt(noise_variance)/std(NOISE)*NOISE;
Y=X+NOISE; 其中X是纯信号，filepath_name是指定噪声文件(.wav)的路径和文件名，SNR是要求的信噪比，fs是信号X的采样频率，Y是带噪信号，NOISE是叠加在信号上的噪声。 3，检验带噪信号的信噪比 信噪比的定义为 信号能量 (纯信号)^2 SNR=-----------------=-------------------------- 噪声能量 (带噪信号-纯信号)^2
function snr=SNR_singlech(I,In)
% 计算信噪比函数
% I :\original signal
% In :noisy signal(ie. original signal + noise signal)
snr=0;
Ps=sum(sum((I-mean(mean(I))).^2));%signal power
Pn=sum(sum((I-In).^2)); %noise power
snr=10*log10(Ps/Pn); 其中I是纯信号，In是带噪信号，snr是信噪比 以下给出调用上函数的例子可作参考： 例一
clear all; clc; close all;
[filename,pathname]=uigetfile('*.wav','请选择语音文件：');
[X,fs]=wavread([pathname filename]);
[Y,NOISE] = noisegen(X,10);
subplot 311; plot(X);
subplot 312; plot(NOISE);
subplot 313; plot(Y);
mn=mean(NOISE)
snr=SNR_singlech(X,Y) 例二
clear all; clc; close all;
[filename,pathname]=uigetfile('*.wav','请选择语音文件：');
[filename1,pathname1]=uigetfile('*.wav','请选择噪声文件：');
filepath_name=[pathname1 filename1];
[X,fs]=wavread([pathname filename]);
[Y,NOISE] = add_noisem(X,filepath_name,10,fs);
subplot 311; plot(X);
subplot 312; plot(NOISE);
subplot 313; plot(Y);
mn=mean(NOISE)
snr=SNR_singlech(X,Y) 参考： [1] .