Independent Component Analysis (ICA)

Companion code to the project done for the MVA course Probabilistic Graphical Models on Independent Component Analysis. It includes:

• A Numpy implementation of the FastICA algorithm
• Two jax implementation of the FastICA Algorithm, one with a discriminating prior depending on the estimated (non) -Gaussianity of each source.
• A Jax implementation of the Gradient Descent for the maximum-likelihood estimator, with a discriminating prior depending on the estimated (non)-Gaussianity of each source.
• A Flax (Jax) implementation of identifiable Variational Autoencoder (iVAE) for ICA

Full report available at here

Audio Source Separation Using ICA

Here are some audio samples of the source separation results:

Speech signals

• Original Mixed Audio: Listen
• Separated Audio Source 1: Listen
• Separated Audio Source 2: Listen
• Separated Audio Source 3: Listen

Sound signals

• Original Mixed Audio: Listen
• Separated Audio Source 1: Listen
• Separated Audio Source 2: Listen

References

• Independent Component Analysis: Algorithms and Applications
• Variational Autoencoders and Nonlinear ICA: A Unifying Framework

Authors

Zineb Bentires, Nour Bouayed, Yvann Le Fay