can anyone explain this coding to me? i dont understand it at all?

function test_project
clc;
close all;
clear all;

data1=[0 0 0 0];data2=[0 0 0 0];data3=[0 0 0 0];data4=[0 0 0 0];data=[0 0 0 0];val1=[0 0 0 0];
for i=1:100
 for i=1:10
      fs=22050;
      y1=wavrecord(fs,fs,1);
      wavwrite(y1,fs,16,'user.wav');
      a=wavread('user.wav');
      wavplay(a,fs);
      n=input('Enter 0');
      if(n==0)
          break;
      else
          disp('Record your voice again');
      end
end
  filename = '11L.wav' % Calculate the codebook vector.

    cep = learn('11L.wav');
    codebook = vqlbg(cep, 8);
    result(1) = identify(filename, codebook);

    cep = learn('O.wav');
    codebook = vqlbg(cep, 8);   
    result(2) = identify(filename, codebook);

    cep = learn('U.wav');
    codebook = vqlbg(cep, 8);
    result(3) = identify(filename, codebook);

    cep = learn('V.wav');
    codebook = vqlbg(cep, 8);
    result(4) = identify(filename, codebook);

% Test a file.
  if ( ( result(1) < result(2) ) & ( result(1) < result(3) ) & ( result(1) < result(4) ) )   
      'L' 
  end
  if ( ( result(2) < result(1) ) & ( result(2) < result(3) ) & ( result(2) < result(4) ) )   
      'O' 
  end
  if ( ( result(3) < result(2) ) & ( result(3) < result(1) ) & ( result(3) < result(4) ) )   
      'U'  
  end
  if ( ( result(4) < result(2) ) & ( result(4) < result(1) ) & ( result(4) < result(3) ) )   
      'V' 
  end

% For Checking the Output and Data given to the parallel port 
  Select=input('Enter 0 for TRUE Output ');
  if(Select==0)
      if(ans=='L')
          data=[~val1(:,1) val1(:,2) val1(:,3) val1(:,4)];
      end
      if(ans=='O')
          data=[val1(:,1) ~val1(:,2) val1(:,3) val1(:,4)];
      end
      if(ans=='U')
          data=[val1(:,1) val1(:,2) ~val1(:,3) val1(:,4)];
      end
      if(ans=='V')
          data=[val1(:,1) val1(:,2) val1(:,3) ~val1(:,4)];
      end
      dio = digitalio('parallel','TPI1');
      addline(dio,0:3,'out');
      putvalue(dio.Line(1:4),data);
      val1 = getvalue(dio)
  end
  Close=input('Enter 5 for Exit');
  if(Close==5)
      break;
  end
end
%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          IDENTIFYING THE FILE            %%%%%
function dist = identify(file, codebook)
[filedata, fs] = wavread(file);
truncated = extract(filedata);
cep = mfcc(truncated);
d = disteu(cep', codebook);
dist = sum(min(d,[],2)) / size(d,1);

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          LINDE,BUZO,GRAY VECTOR QUANTIZATION         %%%%%

 function centroid = vqlbg(cepstrum, M)

%           FEATURE MATCHING            

% STEP 1 - INITIALIZATION
centroid = medel(cepstrum);

centroid = centroid';
cepstrum = cepstrum'; 
epsilon = 0.01;
%M = 8; % The size of the codebook
m=1;
while (m<M)
% STEP 2 --- DIVISION
temp_cent = centroid;
j = 1;
for i=1:m
centroid(:,j) = temp_cent(:, i) * (1 + epsilon);
j = j + 1;
centroid(:,j) = temp_cent(:, i) * (1 - epsilon);
j = j + 1;
end
m = 2*m;
test_var = 0;
D_prim = 99999999; % Init D_prim to a sufficient large value.

while (test_var == 0)
% STEP 3 ---  CLUSTERING
dist = disteu(cepstrum, centroid);
[magic, ind] = min(dist, [], 2);

% STEP 4 --- UPDATE

D = sum(sum(dist));

if ( ( (D_prim - D) / D) < epsilon)
test_var = 1;
else
D_prim = D;
end
  end
end

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          FINDING EUCILIDIAN DISTANCE         %%%%%
function d = disteu(x, y)
[M, N] = size(x);
[M2, P] = size(y);

if (M ~= M2)
error('Matrix dimensions do not match.')
end

d = zeros(N, P)
if (N < P)
copies = zeros(1,P);

for n= 1:N
d(n,:) = sum((x(:, n+copies) - y) .^2, 1);
end
else
copies = zeros(1,N);

for p = 1:P
  d(:,p) = sum((x - y(:, p+copies)) .^2, 1)';
  end
end
d = d.^0.5;

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          LEARNING AND MFCC           %%%%%
function cep = learn(file)
[filedata, fs] = wavread(file);
truncated = extract(filedata);
cep = mfcc(truncated);

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          EXTRACTING FEATURES         %%%%%
function sampledata = extract(xin, length)
length=16;
mean = 0;
for i = 1:1000
mean = mean + (abs(xin(i)) / 100);
end
threshold = mean * 2 ;
for first_index = 1:size(xin)
if (abs(xin(first_index)) > threshold)
break;
end
end
for end_index = 1:size(xin)
temp = size(xin) - end_index;
if (abs(xin(temp(1))) > threshold)
break;
end
end
sampledata = xin(first_index:(size(xin) - end_index));

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          MEDEL           %%%%%
function m = medel(v);
[nr_of_rows, nr_of_columns] = size(v);
for i=1:nr_of_columns
m(i) = sum(v(1:nr_of_rows, i)) / nr_of_rows;
end

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          DETERMINING MEL-SPACED FREQUENCY BANK           %%%%%
function m = melfb(p,n, fs)

p=20;            %  number of filters in filterbank
n=256;           %  length of fft
fs=22050;
f0 = 700 / fs;
fn2 = floor(n/2);
lr = log(1 + 0.5/f0) / (p+1);
% convert to fft bin numbers with 0 for DC term
bl = n * (f0 * (exp([0 1 p p+1] * lr) - 1));
b1 = floor(bl(1)) + 1;
b2 = ceil(bl(2));
b3 = floor(bl(3));
b4 = min(fn2, ceil(bl(4))) - 1;
pf = log(1 + (b1:b4)/n/f0) / lr;
fp = floor(pf);
pm = pf - fp;
r = [fp(b2:b4) 1+fp(1:b3)];
c = [b2:b4 1:b3] + 1;
v = 2 * [1-pm(b2:b4) pm(1:b3)];
m = sparse(r, c, v, p, 1+fn2);

%   ----------   MARS    MARS    MARS    MARS    MARS   ----------

%%%%%          MEL-CEPSTRUM            %%%%%
function cepstrum = mfcc(x)

% FRAME BLOCKING
j=1;
i=1;
[s1, s2] = size(x);
%x(1: 256)
while ( (j+256) <= s1)
  for( k=1 : 256)
x_new(i,k) = x(k+j-1);
  end
  i = i+1;
j = j + 256;
end

% WINDOWING

j=1;
i=1;
[s1, s2] = size(x);
w = hamming(256);

while ( (j+256) <= s1)
for( k=1 : 256)
x_new(i,k)=x_new(i,k) * w(k);
end
i = i + 1;
j = j + 256;
end

% FAST FOURIER TRANSFORM

j=1;
i=1;
while ( (j+256) <= s1)
x_new_freq(i,1:256) = fft(x_new(i,1:256));
i = i + 1;
j = j + 256;
end

% MEL FREQUENCY WRAPPING    

nr_of_filters = 20;
m = melfb(nr_of_filters,256, 11000);
n2 =1+floor(256/2);
i=1;
j=1;
while ( (j+256) <= s1)
for (k=1:nr_of_filters)
z_prim = (m * (abs(x_new_freq(i,1:n2)).^2)'); %'

z(i,k) = z_prim(k);
end
j = j + 256;
i = i + 1;
end

i=1;
j=1;
while ( (j+256) <= s1)
cepstrum_prim = dct(z(i,1:nr_of_filters));
for (k=1:nr_of_filters)
cepstrum(i,k) = cepstrum_prim(k);
end
j = j + 256;
i = i + 1;
end
resolution = i-1;

Answer 1

[Jan]

Starting a function (!) with the brute clearing header "clc; close all; clear all;" is not a good sign: The absence of any local variables makes "clear all" useless, except for the surely unwanted clearing of all break points. This let me stop reading the code, because it means, that the author does not exactly know, what he is doing.

"i dont understand it at all" is not explicit enough to create an answer. Should we explain the basics of Matlab like " data1=[0 0 0 0] "? It is hard to explain the m,eaning of lines like:

'L'

because this is meaningless. A character is created but not assigned. Due to the missing semicolon it appears in the command windows, but disp('L') would be much nicer.

[UCTI]

then may i enquire what is the purpose for LINDE,BUZO,GRAY VECTOR QUANTIZATION , FINDING EUCILIDIAN DISTANCE as well as DETERMINING MEL-SPACED FREQUENCY BANK then? i dont understand why is it required...sorry, im new in matlab

[Jan]

I do not know LINDE, BUZO and his or her friends. Please read FAQ: Can you explain this article to me. Thanks.