lpc Linear prediction filter coefficients [a,g] = lpc(x) [a,g] = lpc(x,p) [a,g] = lpc(x,p) Determines the coefficients of a pth order forward linear predictor filter by minimizing the squared error. If p is unspecified, a default value of length(x)-1 is used. x: doubleinput signal, if it is a matrix each column is computed independently p: int, natural number, scalarorder of linear predictor filter, value must be scalar, positive and must be less than or equal to length of input signal a: doublecoefficient of forward linear predictor, coefficient for each signal input is returned as a row vector g: doubleColumn vector of averaged square prediction error This function determines coefficients of a forward linear predictor by minimizing prediction error in least squares sense. It is used in Digital Filter Design noise = rand(50000,1,"normal"); //Gaussian White Noise x = filter(1,[1 1/2 1/3 1/4],noise); x = x(45904:50000); [a,g]= lpc(x,3) est_x = filter([0 -a(2:$)],1,x); e = x-est_x; [acs,lags] = xcorr(e,'coeff'); plot(1:97,x(4001:4097),1:97,est_x(4001:4097),'--'); a = gca(); a.grid = [1,1]; title 'Original Signal vs. LPC Estimate'; xlabel 'Sample number', ylabel 'Amplitude'; legend('Original signal','LPC estimate'); | levinson | prony | pyulear | stmcb Ayush Baid