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# schurrc() - Signal Processing

### Syntax

### Example

### Output / Return Value

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### Reference

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.

k = schurrc(r)[k,e] = schurrc(r)

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