title: How to combine mfcc vector with labels from annotation to pass to a neural network
body: <p>Using librosa, I created mfcc for my audio file as follows:</p>
  
  <pre><code>import librosa
  y, sr = librosa.load('myfile.wav')
  print y
  print sr
  mfcc=librosa.feature.mfcc(y=y, sr=sr)
  </code></pre>
  
  <p>I also have a text file that contains manual annotations[start, stop, tag] corresponding to the audio as follows:  </p>
  
  <blockquote>
    <p>0.0 2.0 sound1<br>
    2.0 4.0 sound2<br>
    4.0 6.0 silence<br>
    6.0 8.0  sound1</p>
  </blockquote>
  
  <p>QUESTION: 
  How to do I combine the generated mfcc's that was generated by librosa, with the annotations from text file.</p>
  
  <p>Final goal is, I want to combine mfcc corresponding to the label, and pass<br>
   it to a neural network.<br>
  So a neural network will have the mfcc and corresponding label as training data.     </p>
  
  <p>If it was one dimensional , I could have N columns with N values and the final Column Y with a Class label. 
  But i'm confused how to proceed, as the mfcc has the shape of something like 
  (16, X) or 
  (20, Y). 
  So I don't know how to combine the two.</p>
  
  <p>My sample mfcc's are  here : <a href="https://gist.github.com/manbharae/0a53f8dfef6055feef1d8912044e1418" rel="nofollow noreferrer">https://gist.github.com/manbharae/0a53f8dfef6055feef1d8912044e1418</a></p>
  
  <p>Please help thank you. </p>
  
  <p>Update : Objective is to train a neural network so that it can identify a new sound when it encounters it in the future.  </p>
  
  <p>I googled and found that mfcc are very good for speech. However my audio has speech but I want to indentify non speech. Are there any other recommended audio features for a general purpose audio classification/recognition task?</p>