hello
hello
the logic would be to add x % of rms noise to the rms amplitude of your clean signal
so compute the rms value of each column of your signal , multiply by x/100 *randn(10001 ,1) (and repeat for the others columns)
NB : randn generates a rms = 1 noise (it get's closer to 1 as the number of samples increases)
x = randn(10001,1);
x_rms = sqrt(mean(x.^2))
x_rms = 1.0067
