SPpcXcor

Routine

double SPpcXcor (const float pc[], int Np, float cor[], int Ncof)

Purpose

Convert predictor coefficients to correlation values

Description

This procedure returns Ncor correlations (including cor[0] = 1). Consider a sequence generated by autoregressive process with exactly Np terms. The first Np+1 terms of the autocorrelation function for that sequence match the autocorrelation values that have the Np predictor coefficients as the corresponding optimum predictor values. The remaining autocorrelation values can be uniquely determined from the Np+1 values. These extra values are the same as would be generated with zeros appended to the vector of predictor 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]       first predictor coefficient
  p(i) <==> pc[i-1]     1 <= i < Np

Parameters

<- double SPpcXcor

Normalized prediction error. This is the energy of the prediction residual for a signal with the autocorrelation calculated by this routine applied to a predictor with the given predictor coefficients. Note that this value may be negative if the given predictor coefficients do not correspond to a minimum phase prediction error filter.

-> const float pc[]

Vector of predictor coefficients (Np values). These are the coefficients of the predictor filter, with pc[0] being the predictor coefficient corresponding to lag 1, and pc[Np-1] corresponding to lag Np. The predictor coefficients must correspond to a minimum phase prediction error filter.

-> int Np

Number of predictor coefficients (at most 50)

<- float cor[]

Autocorrelation coefficients (Np+1 values). The first element corresponds to lag zero, while the last element corresponds to lag Np.

-> int Ncof

Number of correlation values to be returned (normally at least Np + 1)

Author / revision

See Also