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HTML Summary of #3493
Denoising source separation
Denoising source separation (PDF)
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Denoising source separation (Indexer Terms)
A new algorithmic framework called denoising source separation (DSS)
is introduced. The main benefit of this framework is that it allows
for easy development of new source separation algorithms which are
optimised for specific problems. In this framework, source
separation algorithms are constucted around denoising
procedures. The resulting algorithms can range from almost blind to
highly specialised source separation algorithms. Both simple linear
and more complex nonlinear or adaptive denoising schemes are
considered. Some existing independent component analysis algorithms
are reinterpreted within DSS framework and new, robust blind source
separation algorithms are suggested. Although DSS algorithms need
not be explicitly based on objective functions, there is often an
implicit objective function that is optimised. The exact relation
between the denoising procedure and the objective function is
derived and a useful approximation of the objective function is
presented. In the experimental section, various DSS schemes are
applied extensively to artificial data, to real
magnetoencephalograms and to simulated CDMA mobile network signals.
Finally, various extensions to the proposed DSS algorithms are
considered. These include nonlinear observation mappings,
hierarchical models and overcomplete, nonorthogonal feature spaces.
With these extensions, DSS appears to have relevance to many
existing models of neural information processing.
2004-02
Denoising source separation
Artificial Intelligence
Machine Learning
Neural Nets
Statistical Models
Särelä
Jaakko
Jaakko Särelä
Valpola
Harri
Harri Valpola