schurrc() - Signal Processing

k = schurrc(r) uses
the Schur algorithm to compute a vector k of reflection
coefficients from a vector r representing an autocorrelation
sequence. k and r are the same
size. The reflection coefficients represent the lattice parameters
of a prediction filter for a signal with the given autocorrelation
sequence, r. When r is a matrix, schurrc treats
each column of r as an independent autocorrelation
sequence, and produces a matrix k, the same size
as r. Each column of k represents
the reflection coefficients for the lattice filter for predicting
the process with the corresponding autocorrelation sequence r.[k,e] = schurrc(r) also
computes the scalar e, the prediction error variance.
When r is a matrix, e is a column
vector. The number of rows of e is the same as
the number of columns of r.

Syntax

k = schurrc(r)[k,e] = schurrc(r)

Example

Reflection Coefficients of Speech Autocorrelation SequenceOpen This Example
Create an autocorrelation sequence from the MATLAB® speech signal contained in mtlb.mat. Use the Schur algorithm to compute the reflection coefficients of a lattice prediction filter for the sequence.
load mtlb
r = xcorr(mtlb(1:5),'unbiased');
k = schurrc(r(5:end))

k =

   -0.7583
    0.1384
    0.7042
   -0.3699

Output / Return Value

Limitations

Alternatives / See Also

Reference