Extracting frequency domain features

We discussed earlier how to convert a signal into the frequency domain. In most modern speech recognition systems, people use frequency-domain features. After you convert a signal into the frequency domain, you need to convert it into a usable form. Mel Frequency Cepstral Coefficients (MFCC) is a good way to do this. MFCC takes the power spectrum of a signal and then uses a combination of filter banks and discrete cosine transform to extract features. If you need a quick refresher, you can check out http://practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs. Make sure that the python_speech_features package is installed before you start. You can find the installation instructions at http://python-speech-features.readthedocs.org/en/latest. Let's take a look at how to extract MFCC features.

How to do it…

1. Create a new Python file, and import the following packages:

   import numpy as np
   import matplotlib.pyplot...