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
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
Fixed-point frequency domain ICA algorithm for the separation
of convolutive mixture of speech (Rajkishore Prasad)
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
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)
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
(ICA) to detailed algorithms in special settings, allowing the prior
information to guide the separation.