tomwalters@0: % method of class @signal
tomwalters@0: % function sig=gennoise
tomwalters@0: %       sig: original @signal with length and samplerate 
tomwalters@0: %       
tomwalters@0: %  This function generates 1/f spatial noise, with a normal error 
tomwalters@0: % distribution (the grid must be at least 10x10 for the errors to be normal). 
tomwalters@0: % 1/f noise is scale invariant, there is no spatial scale for which the 
tomwalters@0: % variance plateaus out, so the process is non-stationary.
tomwalters@0: %
tomwalters@0: %     BETA defines the spectral distribution. 
tomwalters@0: %          Spectral density S(f) = N f^BETA
tomwalters@0: %          (f is the frequency, N is normalisation coeff).
tomwalters@0: %               BETA = 0 is random white noise.  
tomwalters@0: %               BETA  -1 is pink noise
tomwalters@0: %               BETA = -2 is Brownian noise
tomwalters@0: %          The fractal dimension is related to BETA by, D = (6+BETA)/2
tomwalters@0: % 
tomwalters@0: % 
tomwalters@0: %   RETURN VALUE:
tomwalters@0: %       sig:  @signal 
tomwalters@0: %
tomwalters@0: % (c) 2007 ISVR University of Southampton
tomwalters@0: % Stefan Bleeck (bleeck@gmail.com)
tomwalters@0: % $Date: 2003/01/25 12:47:43 $
tomwalters@0: % $Revision: 1.4 $
tomwalters@0: 
tomwalters@0: % function sig=gennoise(sig,beta,rms_desired)
tomwalters@0: function sig=gennoise(sig,beta)
tomwalters@0: 
tomwalters@0: % if nargin<3
tomwalters@0: %    rms_desired=-1; % 
tomwalters@0: % end
tomwalters@0: if nargin<2
tomwalters@0:     beta=0; % 
tomwalters@0: end
tomwalters@0: 
tomwalters@0: sr=getsr(sig);
tomwalters@0: len=getnrpoints(sig);
tomwalters@0: 
tomwalters@0: dots=spatialPattern([1,len],beta);
tomwalters@0: 
tomwalters@0: sig=setvalues(sig,dots);
tomwalters@0: sig=setname(sig,sprintf('Noise with beta = %2.1f',beta));
tomwalters@0: 
tomwalters@0: 
tomwalters@0: % if rms_desired>=0
tomwalters@0: %     sig=rms_desired*sig/rms(sig);
tomwalters@0: % end
tomwalters@0: 
tomwalters@0: