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

**undefined XP**

- 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)