title: Using k-means clustering to classify sounds
body: <p>I am currently trying to classify a sound recorded on my phone, in python.
  What i did up until now is to use FFT to get an array of 4096 samples from the sound, and send that to a .csv file.What I have is the label of the sound(i send that to the csv at the moment of creating it) and an array of values which coresponds to the frequency. The .csv file formating is like this </p>
  
  <pre><code>"label","values"
  "WOOD","3.405,7.549,....etc"
  "WOOD","3.902,6.921,....etc"
  "CERAMIC","1.594,2.954,.....etc"
  </code></pre>
  
  <p>What I'm trying to do is, use k-means clustering to create a model in python, so it can group sounds after their values, and also recognise another sound when I'm testing it. </p>
  
  <p>From my experience, k-means clustering is usually used for a few feats.I am trying to make one with 4096 feats.I searched a lot of possibilites but i didn't find how can i do it with so many of them.</p>
  
  <p>Any idea how can i make it possible?</p>
  
  <p>Thanks.</p>