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<?xml version="1.0" encoding="UTF-8"?>
<!--
*
* This help file was generated from lpc.sci using help_from_sci().
*
-->
<refentry version="5.0-subset Scilab" xml:id="lpc" 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>lpc</refname>
<refpurpose>Linear prediction filter coefficients</refpurpose>
<para> </para>
</refnamediv>
<refsynopsisdiv>
<title>Calling Sequence</title>
<synopsis>
[a,g] = lpc(x)
[a,g] = lpc(x,p)
</synopsis>
<para> </para>
</refsynopsisdiv>
<refsection>
<title>Description</title>
<para>
[a,g] = lpc(x,p)
Determines the coefficients of a pth order forward linear predictor
filter by minimizing the squared error. <para>If p is unspecified, a
default value of length(x)-1 is used.</para>
</para>
<para> </para>
</refsection>
<refsection>
<title>Parameters</title>
<variablelist>
<varlistentry><term>x:</term>
<listitem><para> double</para><para>input signal, if it is a matrix each column is computed independently</para></listitem></varlistentry>
<varlistentry><term>p:</term>
<listitem><para> int, natural number, scalar</para><para>order of linear predictor filter, value must be scalar, positive and must be less than or equal to length of input signal </para></listitem></varlistentry>
<varlistentry><term>a:</term>
<listitem><para> double</para><para>coefficient of forward linear predictor, coefficient for each signal input is returned as a row vector</para></listitem></varlistentry>
<varlistentry><term>g:</term>
<listitem><para> double</para><para>Column vector of averaged square prediction error</para></listitem></varlistentry>
</variablelist>
</refsection>
<refsection>
<title>Description</title>
<para> This function determines coefficients of a forward linear predictor by minimizing prediction error in least squares sense. It is used in Digital Filter Design </para>
<para> </para>
</refsection>
<refsection>
<title>Examples</title>
<programlisting role="example"><![CDATA[
noise = rand(50000,1,"normal"); //Gaussian White Noise
x = filter(1,[1 1/2 1/3 1/4],noise);
x = x(45904:50000);
[a,g]= lpc(x,3)
est_x = filter([0 -a(2:$)],1,x);
e = x-est_x;
[acs,lags] = xcorr(e,'coeff');
plot(1:97,x(4001:4097),1:97,est_x(4001:4097),'--');
a = gca();
a.grid = [1,1];
title 'Original Signal vs. LPC Estimate';
xlabel 'Sample number', ylabel 'Amplitude';
legend('Original signal','LPC estimate');
]]></programlisting>
<scilab:image>
noise = rand(50000,1,"normal"); //Gaussian White Noise
x = filter(1,[1 1/2 1/3 1/4],noise);
x = x(45904:50000);
[a,g]= lpc(x,3)
est_x = filter([0 -a(2:$)],1,x);
e = x-est_x;
[acs,lags] = xcorr(e,'coeff');
plot(1:97,x(4001:4097),1:97,est_x(4001:4097),'--');
a = gca();
a.grid = [1,1];
title 'Original Signal vs. LPC Estimate';
xlabel 'Sample number', ylabel 'Amplitude';
legend('Original signal','LPC estimate');
</scilab:image>
<para> </para>
</refsection>
<refsection>
<title>See also</title>
<simplelist type="inline">
<member><link linkend="aryule">| levinson | prony | pyulear | stmcb</link></member>
</simplelist>
</refsection>
<refsection>
<title>Authors</title>
<simplelist type="vert">
<member>Ayush Baid</member>
</simplelist>
</refsection>
</refentry>
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