One of the first problems presented to students of deep learning is to classify handwritten digits in the MNIST dataset. This was recently ported to the web thanks to deeplearn.js. The web version has distinct educational advantages over the relatively dry TensorFlow tutorial. You can immediately get a feeling for the model, and start building intuition for what works and what doesn’t. Let’s preserve this interactivity, but change domains to audio. This post sets the scene for the auditory equivalent of MNIST. Rather than recognize handwritten digits, we will focus on recognizing spoken commands. We’ll do this by converting sounds like this:

Into images like this, called log-mel spectrograms, and in the next post, feed these images into the same types of models that do handwriting recognition so well:

The audio feature extraction technique I discuss here is generic enough to work for all sorts of audio, not just human speech. The rest of the post explains how. If you don’t care and just want to see the code, or play with some live demos, be my guest! Continue reading →