Calculating features from the envelope
Now that you've removed some of the noisier fluctuations in the audio, let's see if this improves your ability to classify.
audio_rectified_smooth from the previous exercise is available in your workspace.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Exercise instructions
- Calculate the mean, standard deviation, and maximum value for each heartbeat sound.
- Column stack these stats in the same order.
- Use cross-validation to fit a model on each CV iteration.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate stats
means = np.____(audio_rectified_smooth, axis=0)
stds = ____(audio_rectified_smooth, axis=0)
maxs = ____(audio_rectified_smooth, axis=0)
# Create the X and y arrays
X = np.column_stack([____, ____, ____])
y = labels.reshape(-1, 1)
# Fit the model and score on testing data
from sklearn.model_selection import cross_val_score
percent_score = ____(model, ____, ____, cv=5)
print(np.mean(percent_score))