Implementation of MATLAB short-time autocorrelation

The use of voice.txt The contents are as follows , It is composed of many lines

fid=fopen('voice.txt', 'rt');
x=fscanf(fid, '%f');
fclose(fid);

% Rectangular windows do autocorrelation 
s1=x(1: 320);% Take an array x Before 320 A digital 
N=320;
A=[];
for k=1:320
    sum=0;
    for m=1:N-k+1
        sum=sum+s1(m)*s1(m+k-1);% Autocorrelation 
    end
    A(k)=sum;
end

disp(A);
for k=1:320
    A1(k)=A(k)/A(1);   % normalization A(k);
end

%hamming Window autocorrelation 
f=zeros(1, 320);
n=1;
j=1;
while j<=320
    f(1, j)=x(n)*[0.54-0.46*cos(2*pi*n/319)];%x(n)*hamming window 
    j=j+1;
    n=n+1;
end

B=[];
for k=1:320
    sum=0;
    for m=1:N-k+1
        sum=sum+f(m)*f(m+k-1);% Autocorrelation 
    end
    B(k)=sum;
end

for k=1:320
    B1(k)=B(k)/B(1);% normalization 
end


s2=s1/max(s1);
figure(1);
subplot(3,1,1);
plot(s2);
xlabel(' Sample number ');
ylabel(' amplitude ');
axis([0, 320, -1, 1]);

subplot(3, 1, 2);
plot(A1);
xlabel(' Time delay k');
ylabel('R(k)');
axis([0, 320, -1, 1]);

subplot(3,1,3);
plot(B1);
xlabel(' Time delay k');
ylabel('R(k)');
axis([0, 320, -1, 1]);