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<span class="path"><a href="index.html">FOSSEE Signal Processing Toolbox</a> >> <a href="section_e54aa8aac34aa55341e8b4b782fe1a74.html">FOSSEE Signal Processing Toolbox</a> > ac2poly</span>
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<div class="refnamediv"><h1 class="refname">ac2poly</h1>
<p class="refpurpose">Convert autocorrelation sequence to polynomial of prediction filter</p></div>
<div class="refsynopsisdiv"><h3 class="title">Calling Sequence</h3>
<div class="synopsis"><pre><span class="default">a</span><span class="default"> = </span><span class="functionid">ac2poly</span><span class="default">(</span><span class="default">r</span><span class="default">)</span>
<span class="default">[</span><span class="default">a</span><span class="default">,</span><span class="default">e</span><span class="default">] = </span><span class="functionid">ac2poly</span><span class="default">(</span><span class="default">r</span><span class="default">)</span></pre></div></div>
<div class="refsection"><h3 class="title">Parameters</h3>
<dl><dt><span class="term">r:</span>
<dd><p class="para">Autocorrelation sequence to be represented with an FIR linear prediction filter</p></dd></dt>
<dt><span class="term">a:</span>
<dd><p class="para">Output polynomial representing the linear prediction filter e/(a(1) + a(2)z + a(3)z^2 .. a(N)z^N-1)</p></dd></dt>
<dt><span class="term">e:</span>
<dd><p class="para">Output scaling for the lienar prediction filter</p></dd></dt></dl></div>
<div class="refsection"><h3 class="title">Description</h3>
<p class="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.</p>
<p class="para">Author
Parthe Pandit</p>
<p class="para"></p></div>
<div class="refsection"><h3 class="title">Bibliography</h3>
<p class="para">Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988.</p></div>
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