ICA Research Network (ICArn.org)
EPSRC Research Network on Blind Source Separation
and Independent Component Analysis (ICA Research Network)
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A free (GPL) MATLAB program that implements the fast fixed-point algorithm for independent component analysis and projection pursuit. It features an easy-to-use graphical user interface, and a computationally powerful algorithm.

An interactive Matlab toolbox for processing continuous and event-related EEG, MEG and other electrophysiological data using independent component analysis (ICA), time/frequency analysis, and other methods including artifact rejection.

Fixed-point frequency domain ICA algorithm for the separation of convolutive mixture of speech (Rajkishore Prasad)
[Download Matlab code from ICA Central]
"This is fixed-point frequency domain ICA algorithm for the separation of convolutive mixture of speech. The proposed algorithms is based on statistical modeling of time-series of speech spectral components by exponential power distribution and its application in the fixed-point algorithm proposed by Aapo Hyvarinen. This GUI can be used to capture speech signal with two element linear microphone array for separation by fixed-point frequency domain ICA"

OOLABSS is a computer program written in C++ and running under Windows95/NT. It offers an interactive environment for experiments with several online algorithms for Blind Separation of Sources (BSS) and Independent Component Analysis (ICA). OOLABSS is free for non-commercial use.

Two independent demo packages for MATLAB that implement a number of efficient algorithms for ICA employing HOS (higher order statistics), BSS employing SOS (second order statistics) and LP (linear prediction), and BSE (blind signal extraction) employing various SOS and HOS methods.

ICA Code from Pierre Comon

A collection of machine learning algorithms implemented mainly for Matlab, including: Independent component analysis (ICA) and Artificial neural networks (ANN)

DSS: Denoising Source Separation for Matlab
This is a general framework for source separation called denoising source separation (DSS), where source separation is constructed around denoising procedures. The DSS algorithms may vary from almost blind (ICA) to detailed algorithms in special settings, allowing the prior information to guide the separation.


This page updated 09-Jan-2007