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<document xmlns="http://cnx.rice.edu/cnxml" xmlns:md="http://cnx.rice.edu/mdml/0.4" xmlns:m="http://www.w3.org/1998/Math/MathML" xmlns:bib="http://bibtexml.sf.net/" id="Module.2003-12-08.1132">
  <name>Music Classification by Genre: Overall Results</name>
  <metadata>
  <md:version>**new**</md:version>
  <md:created>2003/12/08 21:11:32.536 US/Central</md:created>
  <md:revised>2003/12/08 21:13:47.184 US/Central</md:revised>
  <md:authorlist>
    <md:author id="mitali">
      <md:firstname>Mitali</md:firstname>
      
      <md:surname>Banerjee</md:surname>
      <md:email>mitali@rice.edu</md:email>
    </md:author>
    <md:author id="mchu">
      <md:firstname>Melodie</md:firstname>
      <md:othername>M</md:othername>
      <md:surname>Chu</md:surname>
      <md:email>mchu@rice.edu</md:email>
    </md:author>
    <md:author id="chunter">
      <md:firstname>Christopher</md:firstname>
      <md:othername>Robert</md:othername>
      <md:surname>Hunter</md:surname>
      <md:email>chunter@rice.edu</md:email>
    </md:author>
  </md:authorlist>

  <md:maintainerlist>
    <md:maintainer id="mitali">
      <md:firstname>Mitali</md:firstname>
      
      <md:surname>Banerjee</md:surname>
      <md:email>mitali@rice.edu</md:email>
    </md:maintainer>
  </md:maintainerlist>
  
  <md:keywordlist>
    <md:keyword>music</md:keyword>
    <md:keyword>classification</md:keyword>
    <md:keyword>genre</md:keyword>
    <md:keyword>overall</md:keyword>
    <md:keyword>results</md:keyword>
  </md:keywordlist>

  <md:abstract>When tested with the training vectors, the system is 87.5% accurate.  Higher accuracy implies that the system has memorized the training set and is unable to generalize when given new inputs.</md:abstract>
</metadata>

  <content>
<figure id="bw">
  <name>Bandwidth</name>
  <media type="image/jpeg" src="standarddeviation.gif"/>
  <caption>
Overall, bandwidth is a good detector for jazz and rap, but poorer in distinguishing between classical, punk, techno, and country, which all have about the same bandwidth.
  </caption>
</figure>

<figure id="tp">
  <name>Beat (Tempo)</name>
  <media type="image/jpeg" src="sdbeat.gif"/>
  <caption>
Rap is the only genre that can be distinguished by its beat.  Classical and jazz have much higher variability in tempo, since they often consist of a long piece subdivided into sections.  
  </caption>
</figure>

<figure id="freqcutoff">
  <name>Frequency Cutoff</name>
  <media type="image/jpeg" src="sdfreqcutoff.gif"/>
  <caption>
Though difficult to isolate any one genre, this analysis tool does separate them into two main categories: (1) Classical, Punk and Country (2) Techno, Jazz, and Rap.
  </caption>
</figure>

<figure id="freqsmoothness">
  <name>Frequency Smoothness</name>
  <media type="image/jpeg" src="sdfreqsmoothness.gif"/>
  <caption>
While freqsmooth does give a different value for each genre, it also gives a radically different value for songs within a given genre. In other words, it does not give a good representation of a genre as a whole. Given the plus and minus standard deviation bars, each genre overlaps heavily.
  </caption>
</figure>

<figure id="hp">
  <name>High Pass Filtering</name>
  <media type="image/jpeg" src="highpass.gif"/>
  <caption>
Like one would expect, classical had the smallest error of any genre tested. This should be intuitive since it uses the lower frequency part of the spectrum. One can think of classical music as being very fluid with few sudden changes in frequency. Conversely, punk and jazz had the highest amount of error, which is a good indication of higher frequencies being utilized. Compared to classical music, these genres are much less fluid and often exhibit rapid changes in tempo. Somewhere between these two extremes are techno, rap and country. 
  </caption>
</figure>

<figure id="totalpwr">
  <name>Total Power</name>
  <media type="image/jpeg" src="sdtotalpower.gif"/>
  <caption>
The standard deviations of rap and techno are very distinct, whereas the others are all about the same value.  Although the average total power of techno may not be a good indicator, the standard deviation should be able to pick out techno.    
  </caption>
</figure>
  
  </content>
  
</document>
