Connectionists: DSS MATLAB package v1-0

[Jaakko Sarela]

Announcement of the stable release (v1-0) of the DSS MATLAB package

We have recently introduced a general framework for source separation called
denoising source separation (DSS, [1]), 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. The framework has already been applied in
several fields (neuroinformatics [1], climate analysis [2], CDMA signal
acquisition [3] and nonlinear ICA for separation of real-life image mixtures
[4], etc.).

The DSS MATLAB package, developed under the GNU GPL, has reached stable
release v1-0. The package is highly customizable and there is a wide
collection of denoising functions readily available. The package includes a
command-line version as well as a graphical user interface.

The package is available via http://www.cis.hut.fi/projects/dss/package/.

Best regards
 
Jaakko Särelä and Harri Valpola

References:
[1] Denoising source separation. J. Särelä and H. Valpola. Journal of Machine
    Learning Research, 6:233-272, 2005. 

    http://www.cis.hut.fi/projects/dss/publications/#sarela05jmlr 

[2] Frequency-Based Separation of Climate Signals.  A. Ilin and H. Valpola.
    In the proceedings of the 9th European Conference on Principles and
    Practice of Knowledge Discovery in Databases (PKDD 2005), Porto, Portugal,
    pp. 519-526, 2005.

    http://www.cis.hut.fi/projects/dss/publications/#ilin05pkdd

[3] A denoising source separation based approach to interference cancellation
    for DS-CDMA array systems.  K. Raju and J. Särelä.  In Proceedings of the
    38th Asilomar Conference on Signals, Systems, and Computers, Pacific
    grove, CA, USA, pp. 1111 -- 1114, 2004.

    http://www.cis.hut.fi/projects/dss/publications/#raju04asilomar

[4] Separation of nonlinear image mixtures by denoising source separation.
    M.S.C. Almeida, H. Valpola and J. Särelä.  In Proceedings of the 6th
    International Conference on Independent Component Analysis and Blind
    Source Separation, ICA 2006, Charleston, SC, USA, accepted.

    http://www.cis.hut.fi/projects/dss/publications/#almeida06ica