/bss_tutorial

Blind Source Separation Tutorial

Primary LanguageJupyter Notebook

Binder

Blind Source Separation Tutorial

Author: Rémi Carloni Gertosio
Year: 2020
Email: remi.carlonigertosio@cea.fr

The goal of this tutorial is to present Blind Source Separation (BSS) problems and the main methods to solve them. This tutorial does not provide in-depth mathematical explanations for every methods; the emphasis is rather on illustrations and intuition.

Table of Contents

  1. Introduction
  2. Principal Component Analysis
  3. Independant Component Analysis
  4. Non-negative matrix factorization
  5. Sparse matrix factorization: the GMCA example
  6. BSS with pictures

Requirements

This tutorial was written with Python 3.7. The following Python libraries need to be installed to run the tutorial:

  • NumPy,
  • Matplotlib,
  • SciPy,
  • Scikit-Learn,
  • Jupyter.

Acknowledgements

The author would like to thank J. Bobin for BSS materials and helpful feedback for producing this tutorial.