Answering Machine vs. Human -- Neural Network Features Selection I've been tasked with creating an artificial neural network that can classify a telephone call as either answered by a human or an answering machine.
My knowledge of audio processing is, mildly put, limited. I'm quite new to machine learning as well, so feel free to question everything.  Here's what my run-of-the-mill backpropagation network looks like so far:
1. 37 input units;
2. One layer of 10 hidden units;
3. One output unit.  

An audio file comes in, and I extract the data like so [X, fs] = wavread(filename). Then, the 37 features I obtain are the following:
1. The length of X;
2 - 21. In 5% increments, the mean of X in each interval (I know);
22. Spread;
23. The mean of short-time energy;
24. Zero crossing rate;
25 - 37. The mean of each in a set of 13 MFCCs.  

Now, since I've obviously jumbled it all together, my question is the following: What do you think would be a good set of features for the problem at hand?  

Thank you for your time
I don't think that you would do this by feature extraction. Instead, I would train a many-to-one LSTM, which classifies the input sequence as -1 or 1. For you, it is easy to collect a huge amount of labeled speech data, just by calling. Note that, the data size is important. 

LSTMs are very powerful techniques and should handle this problem gracefully.