#### Computation of Linear Prediction Coefficients

In the autocorrelation method of linear prediction, the linear prediction coefficients are computed from the Bartlett-window-biased*autocorrelation function*(Chapter 6):

where denotes the th data frame from the signal . To obtain the th-order linear predictor coefficients , we solve the following system of linear

*normal equations*(also called

*Yule-Walker*or

*Wiener-Hopf*equations):

In matlab syntax, the solution is given by `` '', where , and . Since the covariance matrix is symmetric and

*Toeplitz*by construction,

^{11.4}an solution exists using the

*Durbin recursion*.

^{11.5}If the rank of the autocorrelation matrix is , then the solution to (10.12) is

*unique*, and this solution is always

*minimum phase*[162] (

*i.e.*, all roots of are inside the unit circle in the plane [263], so that is always a

*stable*all-pole filter). In practice, the rank of is (with probability 1) whenever includes a noise component. In the noiseless case, if is a sum of sinusoids, each (real) sinusoid at distinct frequency adds 2 to the rank. A dc component, or a component at half the sampling rate, adds 1 to the rank of . The choice of time window for forming a short-time sample autocorrelation and its weighting also affect the rank of . Equation (10.11) applied to a finite-duration frame yields what is called the

*autocorrelation method*of linear prediction [162]. Dividing out the Bartlett-window bias in such a sample autocorrelation yields a result closer to the covariance method of LP. A matlab example is given in §10.3.3 below. The classic covariance method computes an unbiased sample covariance matrix by limiting the summation in (10.11) to a range over which stays within the frame--a so-called ``unwindowed'' method. The autocorrelation method sums over the whole frame and replaces by zero when points outside the frame--a so-called ``windowed'' method (windowed by the rectangular window).

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Linear Prediction Order Selection

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Linear Prediction Methods