Hello Joao,
hello. i can't explain this feedback. perhaps it is due to some iteration
when reading some table. anyway, there is a problem in the
abstraction : noise~, as you say, is not a gaussian noise source. it
is a uniform white noise source. to have a gaussian noise source in pd, i suspect a pd external should
be writen.
You are right. BTW, I already wrote an external for gaussian noise generation (it is an easy adaptation of the noise code + Box-Muller transform to make it gaussian - not distributed yet because I've to clean up the code, it is my first external ;-). But it did not change it very much :-( In addition it seems that using noise~ is still ok to have the desired slope in the spectrum...
thanks,
Alberto
On Sun, 20 Aug 2006, alberto.zin@poste.it wrote:
You are right. BTW, I already wrote an external for gaussian noise generation (it is an easy adaptation of the noise code + Box-Muller transform to make it gaussian - not distributed yet because I've to clean up the code, it is my first external ;-). But it did not change it very much :-( In addition it seems that using noise~ is still ok to have the desired slope in the spectrum...
Doesn't a Gaussian distribution have a spectrum proportional to its own density? That is, exp(-s^2/var)/sqrt(2*pi*var). That means that in the dB vs octave graph, it would appear as a quadratic instead of a straight line, and this doesn't match any of the examples shown in the Colors_of_noise article. Right?
_ _ __ ___ _____ ________ _____________ _____________________ ... | Mathieu Bouchard - tél:+1.514.383.3801 - http://artengine.ca/matju | Freelance Digital Arts Engineer, Montréal QC Canada
On 8/20/06, Mathieu Bouchard matju@artengine.ca wrote:
Doesn't a Gaussian distribution have a spectrum proportional to its own density? That is, exp(-s^2/var)/sqrt(2*pi*var). That means that in the dB vs octave graph, it would appear as a quadratic instead of a straight line, and this doesn't match any of the examples shown in the Colors_of_noise article. Right?
There's a difference. The spectrum of the Gaussian distribution is only valid for single variables. Looking at the spectrum of a vector where each sample is a Gaussian random variable has to be treated differently. The Gaussian noise vector has a flat frequency response.
I think we can use linear phase filter theory to describe the way this works. Our vector is a sum of elementary vectors multiplied by Gaussian random variables. noise_vector, N=sum(j=1,N : e-j * X-j ) where e-j's are elementary vectors and X-j~N(0,var)
The fourier transform is then a sum of linear phase filters N(f)=sum(j=1,N : X-j * e^(2pi* i * j * f/N) )
but that's where I don't know what to do....it's a big sum of complex values multiplied by random variables. I think the expected spectrum is flat, but I'm not sure about the last part...
Chuck