title: How does mfcc feature size affect recurent neural network
body: <p>So I'm learning machine learning and wanted to know how does mfcc feature size affect on RNN (Recurent Neural Network)?</p>
  
  <p>With librosa I extracted mfcc and then delta coefficients and after that I get array of dimension [13, sound_length]</p>
  
  <p>The code of extracting mfcc and delta coefficients with python: (y - sound file data, sr - length of y)</p>
  
  <pre><code>mfcc = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
  mfcc_delta = librosa.feature.delta(mfcc, axis=0, order=1)
  </code></pre>
  
  <p>So theoretically if I want to train network with this kind of data and with data where n_mfcc=39. Who would be better and why? (Ignore all other hyper parameters) I just want to know theoretics of how this parameter affect RNN.</p>