Engineering spectral features
As you can probably tell, there is a lot more information in a spectrogram compared to a raw audio file. By computing the spectral features, you have a much better idea of what's going on. As such, there are all kinds of spectral features that you can compute using the spectrogram as a base. In this exercise, you'll look at a few of these features.
The spectogram spec from the previous exercise is available in your workspace.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
import librosa as lr
# Calculate the spectral centroid and bandwidth for the spectrogram
bandwidths = lr.feature.____(S=____)[0]
centroids = lr.feature.____(S=____)[0]