% m-file to take spectrogram of students' "open" and "give me'
% voce_spc.m
% 7/16/03 - fixed DC offset bug found by student
% small change seen in training because DC offset was small
% 2/23/97, G. Dempsey
% Bradley University, Electrical & Computer Engineering

clear

% get time domain info for 6 students and build a matrix

% truncate to shortest segment y7
ly7=2563;

y1=wavread('h:\ee410\hw8\olee.wav');            % open for Lee
y1=y1(1:ly7);
P=y1;
clear y1

y2=wavread('h:\ee410\hw8\glee.wav');            % give me for Lee
y2=y2(1:ly7);
P=[P y2];
clear y2

y3=wavread('h:\ee410\hw8\ojoel.wav');
y3=y3(1:ly7);
P=[P y3];
clear y3

y4=wavread('h:\ee410\hw8\gjoel.wav');
y4=y4(1:ly7);
P=[P y4];
clear y4

y5=wavread('h:\ee410\hw8\ojeff.wav');
y5=y5(1:ly7);
P=[P y5];
clear y5

y6=wavread('h:\ee410\hw8\gjeff.wav');
y6=y6(1:ly7);
P=[P y6];
clear y6

y7=wavread('h:\ee410\hw8\ocurtis.wav');
y7=y7(1:ly7);
P=[P y7];
clear y7

y8=wavread('h:\ee410\hw8\gcurtis.wav');
y8=y8(1:ly7);
P=[P y8];
clear y8

y9=wavread('h:\ee410\hw8\okurt.wav');
y9=y9(1:ly7);
P=[P y9];
clear y9

y10=wavread('h:\ee410\hw8\gkurt.wav');
y10=y10(1:ly7);
P=[P y10];
clear y10

y11=wavread('h:\ee410\hw8\obecky.wav');
y11=y11(1:ly7);
P=[P y11];
clear y11

y12=wavread('h:\ee410\hw8\gbecky.wav');
y12=y12(1:ly7);
P=[P y12];
clear y12

% South-East U.S. Test Voices

y13=wavread('h:\ee410\hw8\ogary.wav');
y13=y13(1:ly7);
P=[P y13];
clear y13

y14=wavread('h:\ee410\hw8\ggary.wav');
y14=y14(1:ly7);
P=[P y14];
clear y14

y15=wavread('h:\ee410\hw8\odana.wav');
y15=y15(1:ly7);
P=[P y15];
clear y15

y16=wavread('h:\ee410\hw8\gdana.wav');
y16=y16(1:ly7);
P=[P y16];
clear y16


% eliminate DC content and normalize data

for i=1:16

	m=mean(P(:,i));
	P(:,i)=P(:,i)-m;			% remove DC
	P(:,i)=P(:,i)/max(abs(P(:,i)));	% normalize
end

disp('Compute Spectrogram')


% setup code for frequency response of input signals (fft)

Npts=256;               % 256 pt FFT to distinquish freuencies
			% freq div= 8KHz/256= 31.25 Hz

[x1,f,t]=specgram(P(:,1),Npts,8000);	% take spectrogram
x2=specgram(P(:,2),Npts,8000);
x3=specgram(P(:,3),Npts,8000);
x4=specgram(P(:,4),Npts,8000);
x5=specgram(P(:,5),Npts,8000);
x6=specgram(P(:,6),Npts,8000);
x7=specgram(P(:,7),Npts,8000);
x8=specgram(P(:,8),Npts,8000);
x9=specgram(P(:,9),Npts,8000);
x10=specgram(P(:,10),Npts,8000);
x11=specgram(P(:,11),Npts,8000);
x12=specgram(P(:,12),Npts,8000);
x13=specgram(P(:,13),Npts,8000);
x14=specgram(P(:,14),Npts,8000);
x15=specgram(P(:,15),Npts,8000);
x16=specgram(P(:,16),Npts,8000);

clear P

x1=x1(1:48,:);				% look at 0-1500Hz only
					% x1 is 48x19 matrix
x2=x2(1:48,:);
x3=x3(1:48,:);
x4=x4(1:48,:);
x5=x5(1:48,:);
x6=x6(1:48,:);
x7=x7(1:48,:);
x8=x8(1:48,:);
x9=x9(1:48,:);
x10=x10(1:48,:);
x11=x11(1:48,:);
x12=x12(1:48,:);
x13=x13(1:48,:);
x14=x14(1:48,:);
x15=x15(1:48,:);
x16=x16(1:48,:);

					% create 1D matrix for ANN training
count=1;
disp('create 1D matrix for ANN')

for i=1:48

	for j=1:19
	z1(count)=x1(i,j);
	z2(count)=x2(i,j);
	z3(count)=x3(i,j);
	z4(count)=x4(i,j);
	z5(count)=x5(i,j);
	z6(count)=x6(i,j);
	z7(count)=x7(i,j);
	z8(count)=x8(i,j);
	z9(count)=x9(i,j);
	z10(count)=x10(i,j);
	z11(count)=x11(i,j);
	z12(count)=x12(i,j);
	z13(count)=x13(i,j);
	z14(count)=x14(i,j);
	z15(count)=x15(i,j);
	z16(count)=x16(i,j);

	count=count+1;
	end
end

% free up memory

clear x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 x16

% build another Pattern matrix

P=[z1' z2' z3' z4'];
clear z1 z2 z3 z4

P=[P z5' z6' z7' z8'];
clear z5 z6 z7 z8

P=[P z9' z10' z11' z12'];
clear z9 z10 z11 z12

P=[P z13' z14' z15' z16'];
clear z13 z14 z15 z16


% take absolute value and normalize data
disp('normalize spectrogram data')

for i=1:16

P(:,i)=abs(P(:,i));
m=max(P(:,i));
P(:,i)=P(:,i)/m;

end