function stsa_weuclid(filename,outfile,p)

%
%  Implements the  Bayesian estimator based on the weighted-Euclidean
%  distortion measure [1, Eq. 18].
% 
%  Usage:  stsa_weuclid(noisyFile, outputFile, p)
%           
%         infile - noisy speech file in .wav format
%         outputFile - enhanced output file in .wav format
%         p  - power exponent used in the weighted-Euclidean measure.
%              Valid values for p: p>-2
%  
%
%  Example call:  stsa_weuclid('sp04_babble_sn10.wav','out_weuclid.wav',-1);
%
%  References:
%   [1] Loizou, P. (2005). Speech enhancement based on perceptually motivated 
%       Bayesian estimators of the speech magnitude spectrum. IEEE Trans. on Speech 
%       and Audio Processing, 13(5), 857-869.
%   
% Author: Philipos C. Loizou
%
% Copyright (c) 2006 by Philipos C. Loizou
% $Revision: 0.0 $  $Date: 10/09/2006 $
%-------------------------------------------------------------------------

if nargin<3
    fprintf('Usage: stsa_weuclid(infile.wav,outfile.wav,p) \n');
    fprintf('       where p>-2 \n\n');
    return;
end;

if p<-2,
    error('ERROR!  p needs to be larger than -2.\n\n');
end

[x, Srate, bits]= wavread( filename);	


% =============== Initialize variables ===============

len=floor(20*Srate/1000); % Frame size in samples
if rem(len,2)==1, len=len+1; end;
PERC=50; % window overlap in percent of frame size
len1=floor(len*PERC/100);
len2=len-len1;


win=hamming(len); %tukey(len,PERC);  % define window


% Noise magnitude calculations - assuming that the first 6 frames is noise/silence
%
nFFT=2*len;
nFFT2=len/2;
noise_mean=zeros(nFFT,1);
j=1;
for k=1:6
    noise_mean=noise_mean+abs(fft(win.*x(j:j+len-1),nFFT));
    j=j+len;
end
noise_mu=noise_mean/6;
noise_mu2=noise_mu.^2;

%--- allocate memory and initialize various variables

k=1;
img=sqrt(-1);
x_old=zeros(len1,1);
Nframes=floor(length(x)/len2)-1;
xfinal=zeros(Nframes*len2,1);

%===============================  Start Processing =======================================================
%
k=1;
aa=0.98;
mu=0.98;
eta=0.15;
c=sqrt(pi)/2;
C2=gamma(0.5);

%p=-1;
CC=gamma((p+3)/2)/gamma(p/2+1);
ksi_min=10^(-25/10);

for n=1:Nframes


    insign=win.*x(k:k+len-1);

    %--- Take fourier transform of  frame

    spec=fft(insign,nFFT);
    sig=abs(spec); % compute the magnitude
    sig2=sig.^2;

    gammak=min(sig2./noise_mu2,40);  % post SNR
    if n==1
        ksi=aa+(1-aa)*max(gammak-1,0);
    else
        ksi=aa*Xk_prev./noise_mu2 + (1-aa)*max(gammak-1,0);     % a priori SNR
        ksi=max(ksi_min,ksi);  % limit ksi to -25 dB
    end

    log_sigma_k= gammak.* ksi./ (1+ ksi)- log(1+ ksi);
    vad_decision= sum( log_sigma_k)/ len;    
    if (vad_decision< eta) 
        % noise only frame found
        noise_mu2= mu* noise_mu2+ (1- mu)* sig2;
    end
    % ===end of vad===

    vk=ksi.*gammak./(1+ksi);

    %----- weighted Euclidean distance ------------------------
    if p==-1
            hw=CC*sqrt(vk)./(gammak.*exp(-vk/2).*besseli(0,vk/2));  % if p=-1 use this equation as it's faster
    else
            numer=CC*sqrt(vk).*confhyperg(-(p+1)/2,1,-vk,100);
            denom=gammak.*confhyperg(-p/2,1,-vk,100);
            hw=numer./denom;
    end
    %

    sig=sig.*hw;
    Xk_prev=sig.^2;

    xi_w= ifft( hw .* spec, nFFT);
    xi_w= real( xi_w);


    % --- Overlap and add ---------------
    %
    xfinal(k:k+ len2-1)= x_old+ xi_w(1:len1);
    x_old= xi_w(len1+ 1: len);


    k=k+len2;
end
%========================================================================================


wavwrite(xfinal,Srate,16,outfile);