## SPcovMXpc

#### Routine

double SPcovMXpc (const float *Cov[], float pc[], int Np)

#### Purpose

Find predictor coefficients using the modified covariance method

#### Description

This procedure finds the predictor coefficients for a linear predictor using the modified covariance method which guarantees a minimum phase prediction error filter.

Consider a linear predictor with Np coefficients,

              Np
      y(k) = SUM p(i) x(k-i) ,
             i=1

where x(i) is the input signal. This procedure starts by solving for predictors of increasing order as in the case of the standard covariance method. The modified covariance procedure calculates the ratio of the error signal energies for predictors of successive orders. In the autocorrelation method the residual error energy for an n'th order predictor is given by

                    n         2
      Perr(n) = Ex PROD [1 - k (i)],
                   i=0

where k(i) is a reflection coefficient. In the modified covariance method, the ratios of error energies for the covariance method solution are used to determine the reflection coefficients. The sign of k(i) is determined from the sign of the i'th predictor coefficient. The covariance method predictor coefficients are discarded; instead a set of predictor coefficients is derived from the reflection coefficients. Since the error energy decreases with predictor order, the reflection coefficients are less than unity in magnitude. This also means that the predictor coefficients derived from them correspond to a minimum phase prediction error filter.

The equations for the standard covariance method are

      R p = r,

where R is a symmetric positive definite covariance matrix, p is a vector of filter coefficients and r is a vector of correlation values. The matrix R and and vector r are defined as follows

      R(i,j) = Cov(i,j) = E[x(k-i) x(k-j)],  for 1 <= i,j <= N,
        r(i) = Cov(0,i) = E[x(k) x(k-i)],    for 1 <= i <= N.

The solution is found using a Cholesky decomposition of the matrix R. The resulting mean-square prediction error for the standard covariance method can be expressed as

      perr = Ex - 2 p'r + p'R p
           = Ex - p'r ,

where Ex is the mean-square value of the input signal,

      Ex = Cov(0,0) = E[x(k)^2].

If the coefficient matrix is numerically not positive definite, or if the prediction error energy becomes negative at some stage in the calculation, the remaining predictor coefficients are set to zero.

The expectation operator E[.] is often replaced by a sum over k over a finite interval. Minimization of the prediction error over this interval defines the so-called covariance method for determining the linear prediction coefficients.

Predictor coefficients are usually expressed algebraically as vectors with 1-offset indexing. The correspondence to the 0-offset C-arrays is as follows.

      p(1) <==> pc[0]       predictor coefficient corresponding to lag 1
      p(i) <==> pc[i-1]     1 <= i < Np

#### Parameters

<- double SPcovXpc
  Resulting prediction error energy. This is the prediction error for the coefficients returned by this routine.
-> const float *Cov[]
  Cov is an array of pointers to the rows of an Np+1 by Np+1 symmetric positive definite correlation matrix. Only the lower triangular portion of Cov is accessed. Note that with ANSI C, if the actual parameter is not declared to have the const attribute, an explicit cast to (const float **) is required.
<- const float pc[]
  Np element vector of predictor coefficients. Coefficient pc[i] is the predictor coefficient corresponding to lag i+1.
-> int Np
  Number of predictor coefficients

#### Author / revision

P. Kabal / Revision 1.10 2003/05/09

#### See Also

SPcovXpc, SPcovar

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