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  <name>[OFFLINE DEMO - Orignial by Don Johnson]Discrete Fourier Transform (DFT)</name>
  <metadata>
  <md:version>**new**</md:version>
  <md:created>2005/08/11 11:42:22.215 GMT-5</md:created>
  <md:revised>2005/08/11 11:43:44.567 GMT-5</md:revised>
  <md:authorlist>
      <md:author id="ewang">
      <md:firstname>Eric</md:firstname>
      
      <md:surname>Wang</md:surname>
      <md:email>eric.wang@ni.com</md:email>
    </md:author>
  </md:authorlist>

  <md:maintainerlist>
    <md:maintainer id="ewang">
      <md:firstname>Eric</md:firstname>
      
      <md:surname>Wang</md:surname>
      <md:email>eric.wang@ni.com</md:email>
    </md:maintainer>
  </md:maintainerlist>
  
  <md:keywordlist>
    <md:keyword>Embed</md:keyword>
    <md:keyword>Virtual Instrument</md:keyword>
  </md:keywordlist>

  <md:abstract>The Fourier transform can be computed in discrete-time despite the complications caused by a finite signal and continuous frequency.</md:abstract>
</metadata>
  <content>
    <para id="delete_me">     The discrete-time Fourier transform (and the continuous-time transform as well) can be evaluated when we have an analytic expression for the signal. Suppose we just have a signal, such as the speech signal used in the previous chapter, for which there is no formula. How then would you compute the spectrum? For example, how did we compute a spectrogram such as the one shown in the speech signal example? The Discrete Fourier Transform (DFT) allows the computation of spectra from discrete-time data. While in discrete-time we can exactly calculate spectra, for analog signals no similar exact spectrum computation exists. For analog-signal spectra, use must build special devices, which turn out in most cases to consist of A/D converters and discrete-time computations. Certainly discrete-time spectral analysis is more flexible than continuous-time spectral analysis.</para>   
  </content>
  
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