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  <refnamediv>
    <refname>ac2poly</refname>
    <refpurpose>Convert autocorrelation sequence to polynomial of prediction filter.</refpurpose>
  </refnamediv>


<refsynopsisdiv>
   <title>Syntax</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 linear 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>
</para>
</refsection>

<refsection>
   <title>Examples</title>
   <programlisting role="example"><![CDATA[
a = ac2poly([1, 0.5, 0.2])
[a, e] = ac2poly([1, 0.5, 0.2])

   ]]></programlisting>
</refsection>

<refsection role="see also">
   <title>See also</title>
   <simplelist type="inline">
   <member><link linkend="levinson">levinson</link></member>
   </simplelist>
</refsection>

<refsection>
   <title>Authors</title>
   <simplelist type="vert">
   <member>Parthe Pandit</member>
   </simplelist>
</refsection>

<refsection>
   <title>Bibliography</title>
   <para>Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</para>
</refsection>
</refentry>
