Matlab plot voice recording of the “Abrakakabra”

The original signals

(i)                  Using the program of your choice, average your 10 "abracadabras" from your previous assignment, and plot the result. Be sure to line up the signals prior to the averaging operation. Submit your code with your plot

Cross-correlation of my voices

Shift of of my voices

Average of my voices

(ii)                Plot the result of the autocorrelation Rx[n]for each of your 10 "abracadabra" signals, as well as the average of the 10 autocorrelations. Also, plot the autocorrelation for the averaged signal from part (i) above, and compare and comment the two final plots.

Plot the result of the autocorrelation Rx[n]for each of your 10 "abracadabra" signals

                 

       Plot the autocorrelation for the averaged signal from part (i) above

Comment:

Autocorrelation is used to compare a signal with a time-delayed version of itself, because it can tell us the time interval over which a correlation in noise exits.  The average of autocorrelation plot showed a lot of noises in the signal.  It is difficult to find covariance between the time series.

 Cross-correlation compares 2 random signals, and then defines the average value of the signal at sample time.  The average of cross correlation plot showed less noise compare with the autocorrelation.  Cross-correlation is also a convolution.  

%Minh Anh Nguyen

%ECE: 537 Biomedical Signal Processing; CSU

%email: minhanhnguyen@q.com

 

%Homework 3

close all;

clc;

figure;

%Read in the sound

%returns the sample rate(FS) in Hertz

%N = number sample

%the number of bits per sample (BITS) used to encode the data in the file

ax(1)= subplot (3,2,1);

myvoice1 = audioread('J:\BIOM_Signal_processing\HW2\Aka1_a.wma');

[y1, fs1] = audioread('J:\BIOM_Signal_processing\HW2\Aka1_a.wma');

N = length(myvoice1); 

%%player=audioplayer(y1,N); play(player)

t1 = (0:1:length(myvoice1)-1)/fs1;

plot(t1, myvoice1);

title ('My voice [Abrakakabra]#1 Number of sample 229364')

xlabel('time (sec)');

ylabel(' signal strength')

axis tight

grid on;

ax(2)=subplot (3,2,2);

myvoice2 = audioread('J:\BIOM_Signal_processing\HW2\ala_2.wma');

[y2, fs2] = audioread('J:\BIOM_Signal_processing\HW2\ala_2.wma');

N2 = length(myvoice2);

t2 = (0:1:length(myvoice2)-1)/fs2;

plot(t2, myvoice2);

title ('My voice [repeat Abrakakabra]#2 Number sample 370661')

xlabel('time (sec)');

ylabel(' signal strength')

axis tight

grid on;

ax(3)=subplot (3,2,3);

myvoice3 = audioread('J:\BIOM_Signal_processing\HW2\alka3.wma');

[y3, fs3] = audioread('J:\BIOM_Signal_processing\HW2\alka3.wma');

N3 = length(myvoice3);

t3 = (0:1:length(myvoice3)-1)/fs3;

plot(t3, myvoice3);

title ('My voice [repeat Abrakakabra]#3 number of sample 376790')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

ax(4)= subplot (3,2,4);

myvoice4 = audioread('J:\BIOM_Signal_processing\HW2\Alka_4.wma');

[y4, fs4] = audioread('J:\BIOM_Signal_processing\HW2\Alka_4.wma');

N4 = length(myvoice4);

t4 = (0:1:length(myvoice4)-1)/fs4;

plot(t4, myvoice4);

title ('My voice [repeat Abrakakabra]#4 Number of sample 247798')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

ax(5)= subplot (3,2,5);

myvoice5 = audioread('J:\BIOM_Signal_processing\HW2\Alka_5.wma');

[y5, fs5]= audioread('J:\BIOM_Signal_processing\HW2\Alka_5.wma');

N5 = length(myvoice5);

t5 = (0:1:length(myvoice5)-1)/fs5;

plot(t5, myvoice5);

title ('My voice [repeat Abrakakabra]#5 number of sample 266232')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

ax(5)= subplot (3,2,6);

%ax(6)= subplot (2,2,1);

myvoice6 = audioread('J:\BIOM_Signal_processing\HW2\Aka_6.wma');

[y6, fs6] = audioread('J:\BIOM_Signal_processing\HW2\Aka_6.wma');

N6 = length(myvoice6);

t6 = (0:1:length(myvoice6)-1)/fs6;

plot(t6, myvoice6);

title ('My voice [repeat Abrakakabra]#6 number of sample 241624')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

figure;

ax(7)=subplot (2,2,1);

myvoice7 = audioread('J:\BIOM_Signal_processing\HW2\alk_7.wma');

[y7, fs7] = audioread('J:\BIOM_Signal_processing\HW2\alk_7.wma');

N7 = length(myvoice7);

t7 = (0:1:length(myvoice7)-1)/fs7;

plot(t7, myvoice7);

title ('My voice [repeat Abrakakabra]#7 number of sample 303099')

xlabel('time (sec)');

ylabel('signal strength')  

axis tight

grid on;

ax(8)=subplot (2,2,2);

myvoice8 = audioread('J:\BIOM_Signal_processing\HW2\alk8.wma');

[y8, fs8] = audioread('J:\BIOM_Signal_processing\HW2\alk8.wma');

N8 = length(myvoice8);

t8 = (0:1:length(myvoice8)-1)/fs8;

plot(t8, myvoice8);

title ('My voice [repeat Abrakakabra]#8 number of sample 270333 ')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

%figure;

ax(8)=subplot (2,2,3);

myvoice9 = audioread('J:\BIOM_Signal_processing\HW2\ala_9.wma');

[y9, fs9] = audioread('J:\BIOM_Signal_processing\HW2\ala_9.wma');

N9 = length(myvoice9);

t9 = (0:1:length(myvoice9)-1)/fs9;

plot(t9, myvoice9);

title ('My voice [repeat Abrakakabra]#9 number of sample 206829 ')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

%figure;

ax(9)=subplot (2,2,4);

myvoice10 = audioread('J:\BIOM_Signal_processing\HW2\aka_10.wma');

[y10, fs10] = audioread('J:\BIOM_Signal_processing\HW2\aka_10.wma');

N10 = length(myvoice10);

t10 = (0:1:length(myvoice10)-1)/fs10;

plot(t10, myvoice10);

title ('My voice [repeat Abrakakabra]#10 number of sample 169961 ')

xlabel('time (sec)');

ylabel('signal strength')

axis tight

grid on;

%%linkaxes(ax,'x')  %% link all “x”

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%(i) Using the program of your choice, average your 10 "abracadabras" from your previous %assignment, and plot the result. Be sure to line up the signals prior to the averaging operation. %Submit your code with your plot.

%% line up the signals prior to the averaging operation

%Compute the cross-correlations between the eight pairs of signals. Normalize them so their maximum value is one.

y2new = y2(:,1);   %% create signal in 1D

y1new = y1(:,1);   %% signal 1

y3new = y3(:,1);   %% create signal in 1D

y4new = y4(:,1);   %% create signal in 1D

y5new = y5(:,1);   %% create signal in 1D

y6new = y6(:,1);   %% create signal in 1D

y7new = y7(:,1);   %% create signal in 1D

y8new = y8(:,1);   %% create signal in 1D

y9new = y9(:,1);   %% create signal in 1D

y10new = y10(:,1);   %% create signal in 1D

%%% correlattion of signals. Normalize them so their maximum value is one.

[v21,lag21]=xcorr(y2new,y1new);  %% correlattion of signal 1 and 2

v21 = v21/max(v21);

figure;

subplot (7,1,1);

plot(lag21,v21);

title ('lag of my voice [repeat Abrakakabra]#1 and # 2 ')

xlabel('time (sec)');

ylabel('signal strength');

max(v21);

find(v21 == 1);  %% find the value of signal 1

lag21(find(v21==1));    % find the lag value

%%% Signal 3

[v31,lag31]=xcorr(y3new,y1new);  %% correlattion of signal 1 and 2

v31 = v31/max(v31);

%figure

subplot (7,1,2);

plot(lag31,v31);

title ('lag my voice [repeat Abrakakabra]#1 and # 3 ')

xlabel('time (sec)');

ylabel('signal strength');

max(v31);

find(v31 == 1);  %% find the value of signal 1

lag31(find(v31==1));    % find the lag value

%%% Signal 4

[v41,lag41]=xcorr(y4new,y1new);  %% correlattion of signal 1 and 2

v41 = v41/max(v41);

figure

%subplot (7,1,3);

plot(lag41,v41);

title ('Lag of my voice [repeat Abrakakabra]#1 and # 4 ');

xlabel('time (sec)');

ylabel('signal strength');

max(v41);

find(v41 == 1);  %% find the value of signal 1

lag41(find(v41==1));    % find the lag value

%%% Signal 5

[v51,lag51]=xcorr(y5new,y1new);  %% correlattion of signal 1 and 2

v51 = v51/max(v51);

figure

subplot(8,1,1);

plot(lag51,v51);

title ('lag of my voice [repeat Abrakakabra]#1 and # 5 ');

xlabel('time (sec)');

ylabel('signal strength');

max(v51);

find(v51 == 1);  %% find the value of signal 1

lag51(find(v51==1));    % find the lag value

%%% Signal 6

[v61,lag61]=xcorr(y6new,y1new);  %% correlattion of signal 1 and 2

v61 = v61/max(v61);

figure;

%subplot (8,1,2)

plot(lag61,v61);

title ('Lag of my voice [repeat Abrakakabra]#1 and # 6 ');

xlabel('time (sec)');

ylabel('signal strength')

max(v61);

find(v61 == 1);  %% find the value of signal 1

lag61(find(v61==1));    % find the lag value

%%% Signal 7

[v71,lag71]=xcorr(y7new,y1new);  %% correlattion of signal 1 and 2

v71 = v71/max(v71);

figure

plot(lag71,v71)

title ('Lag of my voice [repeat Abrakakabra]#1 and # 7 ')

xlabel('time (sec)');

ylabel('signal strength')

max(v71)

find(v71 == 1)  %% find the value of signal 1

lag71(find(v71==1))    % find the lag value

%%% Signal 8

[v81,lag81]=xcorr(y8new,y1new);  %% correlattion of signal 1 and 2

v81 = v81/max(v81);

figure

%subplot (8,1,3)

plot(lag81,v81)

title ('Lag of  my voice [repeat Abrakakabra]#1 and # 8 ')

xlabel('time (sec)');

ylabel('signal strength')

max(v81)

find(v81 == 1)  %% find the value of signal 1

lag81(find(v81==1))    % find the lag value

%%% Signal 9

[v91,lag91]=xcorr(y9new,y1new);  %% correlattion of signal 1 and 2

v91 = v91/max(v81);

figure

%subplot (9,1,1)

plot(lag91,v91)

title ('Lag of  my voice [repeat Abrakakabra]#1 and # 9 ')

xlabel('time (sec)');

ylabel('signal strength')

max(v91)

find(v91 == 1)  %% find the value of signal 1

lag91(find(v91==1))    % find the lag value

%%% Signal 10

[v101,lag101]=xcorr(y10new,y1new);  %% correlattion of signal 1 and 2

v101 = v101/max(v101);

figure

%subplot (9,1,2)

plot(lag101,v101)

title ('Lag of  my voice [repeat Abrakakabra]#1 and # 10 ')

xlabel('time (sec)');

ylabel('signal strength')

max(v101)

find(v101 == 1)  %% find the value of signal 1

lag101(find(v101==1))    % find the lag value

%% Shift signal 2 to the right

 figure;

vshift = circshift (y2new, -41806);

plot(vshift)

hold on

plot(y1new,'r')

l1=length (y1new); %% find the length of signal 1

l2=length (vshift); %% find the lenght of signal 2 where you shift

%vshift(1: 229364); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#2 ')

xlabel('time (sec)');

ylabel('signal strength');

figure;

plot (y1new)

hold on

vshiftcut = vshift(1: 229364);

figure

plot(vshiftcut, 'g')

%plot(y1new,'g')

title ('cut the shift signal of voice [repeat Abrakakabra]#2 ')

xlabel('time (sec)');

ylabel('signal strength')

%meanvshiftnew = mean(vshiftcut,y1new); %% take the mean of 2 signal wrong

%still in 1D or values

meanvshiftnew = mean([vshiftcut,y1new],2); %% correct need to 2D

figure

plot (meanvshiftnew)

title ('plot average of my voice [repeat Abrakakabra]')

xlabel('time (sec)');

ylabel('signal strength')

soundsc(meanvshiftnew, 44100)

%% Shift signal3 to the right

 figure

vshift2 = circshift (y3new, 41806);

%subplot (10,1,1)

plot(vshift2)

hold on

plot(y1new,'r')

title ('shift of voice [repeat Abrakakabra]#3 ')

xlabel('time (sec)');

ylabel('signal strength');

l3 = length (vshift2); %% find the lenght of signal 2 where you shift

vshift2(1: 229364); %% cut the signal to the smallest values

%figure

%subplot (10,1,2);

%%plot (vshift2);

%%hold on;

%plot (y1new, 'g');

vshiftcut2 = vshift2(1: 169961);

figure

%subplot (10,1,3);

plot(vshiftcut2);

hold on;

plot(y1new,'g');

title ('cut the shift of voice [repeat Abrakakabra]#3 ');

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal4 to the right

 figure

vshift4 = circshift (y4new, -41806);

plot(vshift4)

hold on

plot(y1new,'r')

length (vshift4) %% find the lenght of signal 2 where you shift

%vshift4(1: 169961); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#4 ')

xlabel('time (sec)');

ylabel('signal strength')

%figure

%plot (vshift4)

%hold on

%plot (y1new, 'g')

vshiftcut4 = vshift4(1: 169961);

figure

plot(vshiftcut4)

hold on

plot(y1new,'g')

title ('cut shift of voice [repeat Abrakakabra]#4 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal 5 to the right

 figure

vshift5 = circshift (y5new, -366232);

plot(vshift5)

hold

hold on

plot(y1new,'r')

l5=length (vshift5); %% find the lenght of signal 2 where you shift

vshift5(1: 229364); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#5 ')

xlabel('time (sec)');

ylabel('signal strength')

%figure

%plot (vshift5)

%hold on

%plot (y1new, 'g')

%title ('voice [repeat Abrakakabra]#5 ')

%xlabel('time (sec)');

%ylabel('signal strength')

vshiftcut5 = vshift5(1: 169961);

figure

plot(vshiftcut5)

hold on

plot(y1new,'g')

title ('cut shift of voice [repeat Abrakakabra]#5 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal6 to the right

 figure

vshift6 = circshift (y6new, 241624); %%OK

plot(vshift6)

hold on

plot(y1new,'r')

l6 =length (vshift6) %% find the lenght of signal 2 where you shift

vshift6(1: 229364); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#6 ')

xlabel('time (sec)');

ylabel('signal strength')

figure

plot (y1new, 'g')

hold on

vshiftcut6 = vshift6(1: 169961);

plot(vshiftcut6)

title ('cut shift of voice [repeat Abrakakabra]#6 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal 7 to the left

 figure

vshift7 = circshift (y7new, -333099);

plot(vshift7)

hold on

plot(y1new,'r');

l7=length (vshift7) %% find the lenght of signal 2 where you shift

vshift7(1: 229364); %% cut the signal to the smallest values'

title ('shift of voice [repeat Abrakakabra]#7 ')

xlabel('time (sec)');

ylabel('signal strength')

figure

plot (y1new, 'g')

hold on

vshiftcut7 = vshift7(1: 169961);

plot(vshiftcut7)

title ('cut shift of voice [repeat Abrakakabra]#7 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal 8 to the left

 figure

vshift8 = circshift (y8new, -670333);

plot(vshift8);

hold on

plot(y1new,'r');

l8=length (vshift8); %% find the lenght of signal 2 where you shift

%%vshift8(1: 229364); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#8 ');

xlabel('time (sec)');

ylabel('signal strength');

figure

plot (y1new);

hold on;

vshiftcut8 = vshift8(1: 169961);

plot(vshiftcut8, 'g')

title ('cut line of voice [repeat Abrakakabra]#8 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal 9 to the left

 figure

vshift9 = circshift (y9new, -179961);

plot(vshift9)

hold on

plot(y1new,'r')

l9=length (vshift9); %% find the lenght of signal 2 where you shift

%vshift9(1: 169961); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#9 ')

xlabel('time (sec)');

ylabel('signal strength')

figure;

plot (y1new, 'r');

hold on;

vshiftcut9 = vshift9(1: 169961);

plot(vshiftcut9, 'r');

title ('cut line of voice [repeat Abrakakabra]#9 ')

xlabel('time (sec)');

ylabel('signal strength')

%% Shift signal10 to the right

 figure

vshift10 = circshift (y10new, 241624);

plot(vshift10)

hold on

plot(y1new,'r')

l10 =length (vshift10) %% find the lenght of signal 2 where you shift

%vshift10(1: 169961); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#10 ')

xlabel('time (sec)');

ylabel('signal strength')

figure

plot (y1new, 'g')

hold on

vshiftcut10 = vshift10(1: 169961);

plot(vshiftcut10)

title ('cut shift of voice [repeat Abrakakabra]#10 ')

xlabel('time (sec)');

ylabel('signal strength')

 figure

vshift1 = circshift (y1new, 20333);

plot(vshift1)

hold on

plot(y1new,'r')

vshift1(1: 169961); %% cut the signal to the smallest values

title ('shift of voice [repeat Abrakakabra]#1 ')

xlabel('time (sec)');

ylabel('signal strength')

plot (y1new)

hold on

vshiftcut1 = vshift1(1: 169961);

figure

plot(vshiftcut1, 'r')

title ('cut line of voice [repeat Abrakakabra]#1 ')

xlabel('time (sec)');

ylabel('signal strength')

meanvshiftnewall = mean([vshiftcut2,vshiftcut2, vshiftcut4, vshiftcut5, vshiftcut6, vshiftcut7, vshiftcut8, vshiftcut9 ,vshiftcut1,y10new],2); %% correct need to 2D

figure

plot (meanvshiftnewall)

title ('plot average of my voice [repeat Abrakakabra]')

xlabel('time (sec)');

ylabel('signal strength')

soundsc(meanvshiftnewall, 44100)

%(ii) Plot the result of the autocorrelation  Rx[n]for each of your 10 "abracadabra" %signals, as well as the average of the 10 autocorrelations. Also, plot the autocorrelation for the %averaged signal from part (i) above, and compare and comment the two final plots.

%% do this for each signal

figure

subplot (4,2,1);

[acy1n,lagy1n] = xcorr(y1new,'coeff');

plot(lagy1n,acy1n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#1')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (4,2,2);

[acy2n,lagy2n] = xcorr(y2new,'coeff');

plot(lagy2n,acy2n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#2')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (4,2,3);

[acy3n,lagy3n] = xcorr(y3new,'coeff');

plot(lagy3n,acy3n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#3')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (4,2,4);

[acy4n,lagy4n] = xcorr(y4new,'coeff');

plot(lagy4n,acy4n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#4')

xlabel('time (sec)');

ylabel('signal strength')

figure;

subplot (5,2,1);

[acy5n,lagy5n] = xcorr(y5new,'coeff');

plot(lagy5n,acy5n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#5')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (5,2,2);

[acy6n,lagy6n] = xcorr(y6new,'coeff');

plot(lagy6n,acy6n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#6')

xlabel('time (sec)');

ylabel('signal strength')

figure;

subplot (5,2,3);

[acy7n,lagy7n] = xcorr(y7new,'coeff');

plot(lagy7n,acy7n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#7')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (5,2,4);

[acy8n,lagy8n] = xcorr(y8new,'coeff');

plot(lagy8n,acy8n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#8')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (6,2,1);

[acy9n,lagy9n] = xcorr(y9new,'coeff');

plot(lagy9n,acy9n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#9')

xlabel('time (sec)');

ylabel('signal strength')

%figure;

subplot (6,2,2);

[acy10n,lagy10n] = xcorr(y10new,'coeff');

plot(lagy10n,acy10n)

xlim([0 200000])

title (' the autocorrelation  Rx[n]for my voice [repeat Abrakakabra]#10')

xlabel('time (sec)');

ylabel('signal strength')

l1 = length(acy1n);

l2 = length(acy2n);

l3 = length(acy3n);

l4 = length(acy4n);

l5 = length(acy5n);

l6 = length(acy6n);

l7 = length(acy7n);

l8 = length(acy8n);

l9 = length(acy9n);

l10 = length(acy10n);

%10 = length(acy10n);

cutsig1 = acy1n(1: 339921);

cutsig2 = acy2n(1: 339921);

cutsig3 = acy3n(1: 339921);

cutsig4 = acy4n(1: 339921);

cutsig5 = acy5n(1: 339921);

cutsig6 = acy6n(1: 339921);

cutsig7 = acy7n(1: 339921);

cutsig8 = acy8n(1: 339921);

cutsig9 = acy9n(1: 339921);

cutsig10 = acy10n(1: 339921);

mean1= mean([cutsig2,cutsig1, cutsig3, cutsig4, cutsig5, cutsig6, cutsig7, cutsig8, cutsig9, cutsig10],2);

figure

plot (mean1);

title (' Average of the autocorrelation Rx[n]for my voice ')

xlabel('time (sec)');

ylabel('signal strength');