<?xml version="1.0" encoding="UTF-8"?> <!-- * * This help file was generated from ac2poly.sci using help_from_sci(). * --> <refentry version="5.0-subset Scilab" xml:id="ac2poly" xml:lang="en" xmlns="http://docbook.org/ns/docbook" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:svg="http://www.w3.org/2000/svg" xmlns:ns3="http://www.w3.org/1999/xhtml" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:scilab="http://www.scilab.org" xmlns:db="http://docbook.org/ns/docbook"> <refnamediv> <refname>ac2poly</refname> <refpurpose>Convert autocorrelation sequence to polynomial of prediction filter</refpurpose> </refnamediv> <refsynopsisdiv> <title>Calling Sequence</title> <synopsis> a = ac2poly(r) [a,e] = ac2poly(r) </synopsis> </refsynopsisdiv> <refsection> <title>Parameters</title> <variablelist> <varlistentry><term>r:</term> <listitem><para> Autocorrelation sequence to be represented with an FIR linear prediction filter</para></listitem></varlistentry> <varlistentry><term>a:</term> <listitem><para> Output polynomial representing the linear prediction filter e/(a(1) + a(2)z + a(3)z^2 .. a(N)z^N-1)</para></listitem></varlistentry> <varlistentry><term>e:</term> <listitem><para> Output scaling for the lienar prediction filter</para></listitem></varlistentry> </variablelist> </refsection> <refsection> <title>Description</title> <para> Function ac2poly() finds the best fit polynomial for FIR linear prediction filter a, corresponding to the autocorrelation sequence r. a is the same length as r, and is normalized with the first element. So a(1) = 1. </para> <para> Author Parthe Pandit </para> <para> </para> </refsection> <refsection> <title>Bibliography</title> <para>kartik Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</para> </refsection> </refentry>