schurrc - Compute reflection coefficients from autocorrelation sequence
Syntax
k = schurrc(r)
[k,e] = schurrc(r)
Description
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.
Examples
Create an autocorrelation sequence from the MATLAB speech
signal contained in mtlb.mat, and use the Schur
algorithm to compute the reflection coefficients of a lattice prediction
filter for this autocorrelation sequence:
load mtlb
r = xcorr(mtlb(1:5),'unbiased');
k = schurrc(r(5:end))
k =
-0.7583
0.1384
0.7042
-0.3699
References
[1] Proakis, J. and D. Manolakis, Digital
Signal Processing: Principles, Algorithms, and Applications,
Third edition, Prentice-Hall, 1996, pp.868-873.
See Also
levinson
 | sawtooth | | seqperiod |  |
Includes the most popular MATLAB recorded presentations with Q&A sessions led by MATLAB experts.
Get the Interactive Kit
Store 
