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  3. Machine Learning for Time Series Data in Python

Exercise

Derivative features: The tempogram

One benefit of cleaning up your data is that it lets you compute more sophisticated features. For example, the envelope calculation you performed is a common technique in computing tempo and rhythm features. In this exercise, you'll use librosa to compute some tempo and rhythm features for heartbeat data, and fit a model once more.

Note that librosa functions tend to only operate on numpy arrays instead of DataFrames, so we'll access our Pandas data as a Numpy array with the .values attribute.

Instructions 1/2

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  • Use librosa to calculate a tempogram of each heartbeat audio.
  • Calculate the mean, standard deviation, and maximum of each tempogram (this time using DataFrame methods)