ieaou_error_signal.m

来自「LPC分析是进行语音分析的有效技术之一」· M 代码 · 共 48 行

news = fopen('ieaou.pcm' ,'rb');
x = fread(news , 'short')/2^15;
fclose(news);
LENGTH=320 ;                                % length of x[n]
fs=8000;                                          % sampling rate 
n=0:1/fs:(LENGTH-1)/fs; 
x_i=x(20001:20320);
x_e=x(32001:32320);
x_a=x(48001:48320);
x_o=x(64001:64320);
x_u=x(80001:80320);
x=[x_i x_e x_a x_o x_u];

% original speech signal %
subplot(5,2,1),plot(n*1000, x(:,1))
xlabel('time[msec]'), ylabel('/i/'),title('Signal')
subplot(5,2,3),plot(n*1000, x(:,2)),ylabel('/e/')
subplot(5,2,5),plot(n*1000, x(:,3)),ylabel('/a/')
subplot(5,2,7),plot(n*1000, x(:,4)),ylabel('/o/')
subplot(5,2,9),plot(n*1000, x(:,5)),ylabel('/u/')

order=12;                                         % order
[a,g]=lpc(x_i,order);                               % predictor coefficients
est_x_i=filter([0 -a(2:end)],1,x_i);                  % Estimated signal
error_i=x_i-est_x_i;                                    % Prediction error
subplot(5,2,2),plot(n*1000,error_i)
xlabel('time[msec]'),title('Prediction Error')

[a,g]=lpc(x_e,order);                               % predictor coefficients
est_x_e=filter([0 -a(2:end)],1,x_e);                  % Estimated signal
error_e=x_e-est_x_e;
subplot(5,2,4),plot(n*1000,error_e)

[a,g]=lpc(x_a,order);                               % predictor coefficients
est_x_a=filter([0 -a(2:end)],1,x_a);                  % Estimated signal
error_a=x_a-est_x_a;
subplot(5,2,6),plot(n*1000,error_a)

[a,g]=lpc(x_o,order);                               % predictor coefficients
est_x_o=filter([0 -a(2:end)],1,x_o);                  % Estimated signal
error_o=x_o-est_x_o;
subplot(5,2,8),plot(n*1000,error_o)

[a,g]=lpc(x_u,order);                               % predictor coefficients
est_x_u=filter([0 -a(2:end)],1,x_u);                  % Estimated signal
error_u=x_u-est_x_u;
subplot(5,2,10),plot(n*1000,error_u)