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author Daniele Nicolodi <nicolodi@science.unitn.it>
date Mon, 05 Dec 2011 16:20:06 +0100
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  <title>Using spectral windows (LTPDA Toolbox)</title>
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  <p style="font-size:1px;">&nbsp;</p>

  <table class="nav" summary="Navigation aid" border="0" width=
  "100%" cellpadding="0" cellspacing="0">
    <tr>
      <td valign="baseline"><b>LTPDA Toolbox</b></td><td><a href="../helptoc.html">contents</a></td>

      <td valign="baseline" align="right"><a href=
      "specwin_description.html"><img src="b_prev.gif" border="0" align=
      "bottom" alt="What are LTPDA spectral windows?"></a>&nbsp;&nbsp;&nbsp;<a href=
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      "bottom" alt="Spectral Estimation Methods"></a></td>
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  <h1 class="title"><a name="f3-12899" id="f3-12899"></a>Using spectral windows</h1>
  <hr>
  
  <p>
	<p>
  Spectral windows are typically used in spectral analysis algorithms. In all LTPDA spectral analysis 
  functions, spectral windows are specified as parameters in an input parameter list. The following
  example shows the use of <tt>ao/psd</tt> to estimate an Amplitude Spectral Density of
  the time-series captured in the input AO, <tt>a</tt>. The help for <a href="matlab:doc('ao/ao/psd')">ao/lpsd</a>
  reveals that the required parameter for setting the window function is <tt>'Win'</tt>.
</p>	
<div class="fragment"><pre>
    <br>    <span class="comment">% Parameters</span> 
    nsecs = 1000;
    fs = 10;
    
    <span class="comment">% Create input AOs</span>
    x1 = ao(plist( <span class="string">'waveform'</span>, <span class="string">'sine wave'</span>, <span class="string">'f'</span>,0.1, <span class="string">'A'</span>,1, <span class="string">'nsecs'</span>,nsecs, <span class="string">'fs'</span>,fs));
    x2 = ao(plist( <span class="string">'waveform'</span>, <span class="string">'noise'</span>, <span class="string">'type'</span>, <span class="string">'normal'</span>, <span class="string">'nsecs'</span>,nsecs, <span class="string">'fs'</span>,fs));
       
    <span class="comment">% Add both</span>
    x = x1 + x2;
    
    <span class="comment">% Compute psd with Blackman-Harris window</span>
    z = psd(x,plist(<span class="string">'win'</span>,<span class="string">'BH92'</span>));
        
    <span class="comment">% Plot</span>
    iplot(z);
</pre></div>
<p>
  In this case, the size of the spectral window (number of samples) may not match the length
  of the segments in the spectral estimation. The <tt>psd</tt> algorithm then 
  recomputes the window using the input design but for the correct length of window function.
</p>

<img src="images/specwin_using1.png" >

<h2>Selecting the Peak Side-Lobe level (psll) with Kaiser's window</h2>
<p>
  The <a href="specwin_description.html">table</a> in the previous section shows how each standard spectral window is defined by the 
  Peak Side-Lobe level (<tt>psll</tt>). However, Kaiser's window allows the user to define the <tt>psll</tt> of the window.
</p> 
<p>
  The following example shows the importance of selecting a suitable <tt>psll</tt> according to each application. The example creates 
  1/f noise (in fact, noise generated 
  by a pole-zero model with a pole at low frequencies) and computes the Amplitude Spectrum Density (ASD) with three different <tt>psll</tt> 
  values. The ASD with the lowest value shows a bias at high frequencies compared with the response of the pzmodel used to generate 
  the data (in black). This effect is due to the power added by the high order lobes of the window. The ASD with the highet value of the
  <tt>psll</tt> adds a feature at low frequencies because the main lobe of the window is too wide. Only the 
  middle value gives an estimation of the ASD without adding window related features.   
</p> 

<div class="fragment"><pre>
    <br>    <span class="comment">% Parameters</span>
    nsecs = 10000;
    fs = 1;
    
    <span class="comment">% Create pzmodel with a low frequency pole</span>
    pzm = pzmodel(1e5,[1e-7,0.1],[]);
    
    <span class="comment">% Build (nearly) 1/f noise</span>
    x = ao(plist(<span class="string">'pzmodel'</span>,pzm, <span class="string">'nsecs'</span>,nsecs, <span class="string">'fs'</span>,fs));
    
    <span class="comment">% Compute psd with Blackman-Harris window</span>
    z1 = psd(x,plist(<span class="string">'scale'</span>,<span class="string">'ASD'</span>,<span class="string">'win'</span>,<span class="string">'Kaiser'</span>,<span class="string">'psll'</span>,50));
    z1.setName(<span class="string">'psll = 50'</span>);
    z2 = psd(x,plist('scale',<span class="string">'ASD'</span>,<span class="string">'win'</span>,<span class="string">'Kaiser'</span>,<span class="string">'psll'</span>,100));
    z2.setName(<span class="string">'psll = 100'</span>);
    z3 = psd(x,plist(<span class="string">'scale'</span>,<span class="string">'ASD'</span>,<span class="string">'win'</span>,<span class="string">'Kaiser'</span>,<span class="string">'psll'</span>,1000));
    z3.setName(<span class="string">'psll = 1000'</span>);
    
    <span class="comment">% Plot</span>
    r = resp(pzm,plist(<span class="string">'f1'</span>,1e-4,<span class="string">'f2'</span>,1));
    r.setName(<span class="string">'response'</span>)
    r.setPlotinfo(plist(<span class="string">'color'</span>,<span class="string">'k'</span>))
    iplot(z1,z2,z3,abs(r));
</pre></div>

<img src="images/specwin_using2.png" >




  </p>

  <br>
  <br>
  <table class="nav" summary="Navigation aid" border="0" width=
  "100%" cellpadding="0" cellspacing="0">
    <tr valign="top">
      <td align="left" width="20"><a href="specwin_description.html"><img src=
      "b_prev.gif" border="0" align="bottom" alt=
      "What are LTPDA spectral windows?"></a>&nbsp;</td>

      <td align="left">What are LTPDA spectral windows?</td>

      <td>&nbsp;</td>

      <td align="right">Spectral Estimation Methods</td>

      <td align="right" width="20"><a href=
      "sigproc_methods.html"><img src="b_next.gif" border="0" align=
      "bottom" alt="Spectral Estimation Methods"></a></td>
    </tr>
  </table><br>

  <p class="copy">&copy;LTP Team</p>
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