Blind Source Separation

# Introduction

In this lab, we will use FastICA to perform blind source separation on a mixed signal. Blind source separation is a technique used to separate mixed signals into their original independent components. This is useful in various fields such as signal processing, image processing, and data analysis. We will use Python's scikit-learn library to perform ICA and PCA on a sample mixed signal.

## VM Tips

After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice.

Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook.

If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.