function Generate_mcTrainData_cut(WSJ_dir_name, save_dir)
  %
  % Input variables:
  %    WSJ_dir_name: string name of WAV file directory converted from original wsjcam0 SPHERE files
  %                  (*Directory structure for wsjcam0 corpus to be kept as it is after obtaining it from LDC. 
  %                    Otherwise this script does not work.)
  %
  % This function generates multi-condition traiing data
  % based on the following items:
  %  1. wsjcam0 corpus (WAV files)
  %  2. room impulse responses (ones under ./RIR/)
  %  3. noise (ones under ./NOISE/).
  % Generated data has the same directory structure as original wsjcam0 corpus. 
  %
  
  if nargin<2
     error('Usage: Generate_mcTrainData(WSJCAM0_data_path, save_dir)  *Note that the input variable WSJCAM0_data_path should indicate the directory name of your clean WSJCAM0 corpus. '); 
  end
  if exist([WSJ_dir_name,'/data/'])==0
     error(['Could not find wsjcam0 corpus : Please confirm if ',WSJ_dir_name,' is a correct path to your clean WSJCAM0 corpus']); 
  end
  
  if ~exist('save_dir', 'var')
      error('You have to set the save_dir variable in the code before running this script!')
  end
  
  display(['Name of directory for original wsjcam0: ',WSJ_dir_name])
  display(['Name of directory to save generated multi-condition training data: ',save_dir])
  
  % Parameters related to acoustic conditions
  SNRdB=20;
  
  % List of WSJ speech data
  flist1='etc/audio_si_tr.lst';
  
  %
  % List of RIRs
  %
  num_RIRvar=24;
  RIR_sim1='./RIR/RIR_SmallRoom1_near_AnglA.wav'; 
  RIR_sim2='./RIR/RIR_SmallRoom1_near_AnglB.wav'; 
  RIR_sim3='./RIR/RIR_SmallRoom1_far_AnglA.wav';  
  RIR_sim4='./RIR/RIR_SmallRoom1_far_AnglB.wav';  
  RIR_sim5='./RIR/RIR_MediumRoom1_near_AnglA.wav';
  RIR_sim6='./RIR/RIR_MediumRoom1_near_AnglB.wav';
  RIR_sim7='./RIR/RIR_MediumRoom1_far_AnglA.wav'; 
  RIR_sim8='./RIR/RIR_MediumRoom1_far_AnglB.wav'; 
  RIR_sim9='./RIR/RIR_LargeRoom1_near_AnglA.wav'; 
  RIR_sim10='./RIR/RIR_LargeRoom1_near_AnglB.wav';
  RIR_sim11='./RIR/RIR_LargeRoom1_far_AnglA.wav'; 
  RIR_sim12='./RIR/RIR_LargeRoom1_far_AnglB.wav'; 
  RIR_sim13='./RIR/RIR_SmallRoom2_near_AnglA.wav';
  RIR_sim14='./RIR/RIR_SmallRoom2_near_AnglB.wav';
  RIR_sim15='./RIR/RIR_SmallRoom2_far_AnglA.wav'; 
  RIR_sim16='./RIR/RIR_SmallRoom2_far_AnglB.wav'; 
  RIR_sim17='./RIR/RIR_MediumRoom2_near_AnglA.wav';
  RIR_sim18='./RIR/RIR_MediumRoom2_near_AnglB.wav';
  RIR_sim19='./RIR/RIR_MediumRoom2_far_AnglA.wav'; 
  RIR_sim20='./RIR/RIR_MediumRoom2_far_AnglB.wav'; 
  RIR_sim21='./RIR/RIR_LargeRoom2_near_AnglA.wav'; 
  RIR_sim22='./RIR/RIR_LargeRoom2_near_AnglB.wav'; 
  RIR_sim23='./RIR/RIR_LargeRoom2_far_AnglA.wav';  
  RIR_sim24='./RIR/RIR_LargeRoom2_far_AnglB.wav';  
  
  %
  % List of noise
  % 
  num_NOISEvar=6;
  noise_sim1='./NOISE/Noise_SmallRoom1';
  noise_sim2='./NOISE/Noise_MediumRoom1';
  noise_sim3='./NOISE/Noise_LargeRoom1';
  noise_sim4='./NOISE/Noise_SmallRoom2';
  noise_sim5='./NOISE/Noise_MediumRoom2';
  noise_sim6='./NOISE/Noise_LargeRoom2';
  
  %
  % Start generating noisy reverberant data with creating new directories
  %
  
  fcount=1;
  rcount=1;
  ncount=1;
  
  if save_dir(end)=='/';
      save_dir_tr=[save_dir,'data/mc_train/'];
  else
      save_dir_tr=[save_dir,'/data/mc_train/'];
  end
  mkdir([save_dir_tr]);
  
  mic_idx=['A';'B';'C';'D';'E';'F';'G';'H'];
  prev_fname='dummy';
  
  for nlist=1:1
      % Open file list
      eval(['fid=fopen(flist',num2str(nlist),',''r'');']);
  
      while 1
          
          % Set data file name
          fname=fgetl(fid);
          if ~ischar(fname);
              break;
          end
          
          idx1=find(fname=='/');  
          
          % Make directory if there isn't any
          if ~strcmp(prev_fname,fname(1:idx1(end)))
              mkdir([save_dir_tr fname(1:idx1(end))])
          end
          prev_fname=fname(1:idx1(end));
         
          % load speech signal
          x=audioread([WSJ_dir_name, '/data/', fname, '.wav'])';
          
          % load RIR and noise for "THIS" utterance
          eval(['RIR=audioread(RIR_sim',num2str(rcount),');']);
          eval(['NOISE=audioread([noise_sim',num2str(ceil(rcount/4)),',''_',num2str(ncount),'.wav'']);']);
  
          % Generate 8ch noisy reverberant data        
          y=gen_obs(x,RIR,NOISE,SNRdB);
  
          % cut to length of original signal
          y = y(1:size(x,2),:);
          
          % rotine to cyclicly switch RIRs and noise, utterance by utterance 
          rcount=rcount+1;
          if rcount>num_RIRvar;rcount=1;ncount=ncount+1;end
          if ncount>10;ncount=1;end
  
          % save the data
  
          y=y/4; % common normalization to all the data to prevent clipping
                 % denominator was decided experimentally
  
          for ch=1:8
  	    outfilename = [save_dir_tr, fname, '_ch', num2str(ch), '.wav'];
              eval(['audiowrite(outfilename, y(:,',num2str(ch),'), 16000);']);
          end
             
          display(['sentence ',num2str(fcount),' (out of 7861) finished! (Multi-condition training data)'])
          fcount=fcount+1;
  
      end
  end
  
  
  %%%%
  function [y]=gen_obs(x,RIR,NOISE,SNRdB)
  % function to generate noisy reverberant data
  
  x=x';
  
  % calculate direct+early reflection signal for calculating SNR
  [val,delay]=max(RIR(:,1));
  before_impulse=floor(16000*0.001);
  after_impulse=floor(16000*0.05);
  RIR_direct=RIR(delay-before_impulse:delay+after_impulse,1);
  direct_signal=fconv(x,RIR_direct);
  
  % obtain reverberant speech
  for ch=1:8
      rev_y(:,ch)=fconv(x,RIR(:,ch));
  end
  
  % normalize noise data according to the prefixed SNR value
  NOISE=NOISE(1:size(rev_y,1),:);
  NOISE_ref=NOISE(:,1);
  
  iPn = diag(1./mean(NOISE_ref.^2,1));
  Px = diag(mean(direct_signal.^2,1));
  Msnr = sqrt(10^(-SNRdB/10)*iPn*Px);
  scaled_NOISE = NOISE*Msnr;
  y = rev_y + scaled_NOISE;
  y = y(delay:end,:);
  
  
  %%%%
  function [y]=fconv(x, h)
  %FCONV Fast Convolution
  %   [y] = FCONV(x, h) convolves x and h, and normalizes the output  
  %         to +-1.
  %
  %      x = input vector
  %      h = input vector
  % 
  %      See also CONV
  %
  %   NOTES:
  %
  %   1) I have a short article explaining what a convolution is.  It
  %      is available at http://stevem.us/fconv.html.
  %
  %
  %Version 1.0
  %Coded by: Stephen G. McGovern, 2003-2004.
  %
  %Copyright (c) 2003, Stephen McGovern
  %All rights reserved.
  %
  %THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  %AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  %IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
  %ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
  %LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
  %CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
  %SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
  %INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
  %CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
  %ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
  %POSSIBILITY OF SUCH DAMAGE.
  
  Ly=length(x)+length(h)-1;  % 
  Ly2=pow2(nextpow2(Ly));    % Find smallest power of 2 that is > Ly
  X=fft(x, Ly2);		   % Fast Fourier transform
  H=fft(h, Ly2);	           % Fast Fourier transform
  Y=X.*H;        	           % 
  y=real(ifft(Y, Ly2));      % Inverse fast Fourier transform
  y=y(1:1:Ly);               % Take just the first N elements