Signal Toolkit - lpc

Function File: a = lpc (x)

Function File: a = lpc (x, p)

Function File: [a, g] = lpc (…)

Function File: [a, g] = lpc (x, p)

Determines the forward linear predictor by minimizing the prediction error in the least squares sense. Use the Durbin-Levinson algorithm to solve the Yule-Walker equations obtained by the autocorrelation of the input signal.

x is a data vector used to estimate the lpc model of p-th order, given by the prediction polynomial a = [1 a(2) … a(p+1)]. If p is not provided, length(p) - 1 is used as default.

x might also be a matrix, in which case each column is regarded as a separate signal. lpc will return a model estimate for each column of x.

g is the variance (power) of the prediction error for each signal in x.

See also: aryule,levinson.