Blind Source Separation

# Blind Source Separation

#### Blind Source Separation Using Temporal Predictability

The method described in the paper, "Blind Source Separation Using Temporal
Predictability" (Neural Computation, 13(7), July, 2001,
View Abstract).

The method is summarised
here,
and here is the
MatLab code.
A key feature of this code is that it executes source separation of 1D signals using a fast
eigenvalue routine.

### Independent Component Analysis Matlab Code

MatLab code for using independent component analysis (ICA) can be
downloaded from here. Summary information about this code can be
viewed from the
README file.
This code is based on the
method described in Bell and Sejnowski's paper "An Information-Maximization
Approach to Blind Separation and Blind Deconvolution" (Neural Computation,
7, 1129-1159, 1995). Key features of this code are that 1) it uses a
conjugate gradient method, and does not therefore require a learning rate
parameter, 2) it executes spatial or temporal or spatiotemporal ICA of 2D
images, 3) it has the option to specify a skewed pdf.
The above MatLab code can be downloaded from
here.

Code for non-square unmixing matrices (with more mixtures than sources)
is also avilable from
here.
The paper that describes the method can
be found here ("Undercomplete
Independent Component Analysis for Signal Separation and Dimension
Reduction").