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キーワード:
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要旨:
In this work we propose a kernel-based blind source separation
(BSS) algorithm that can perform nonlinear BSS
for general invertible nonlinearities. For our kTDSEP algorithm
we have to go through four steps: (i) adapting to
the intrinsic dimension of the data mapped to feature space
F, (ii) finding an orthonormal basis of this submanifold,
(iii) mapping the data into the subspace of F spanned by
this orthonormal basis, and (iv) applying temporal decorrelation
BSS (TDSEP) to the mapped data. After demixing
we get a number of irrelevant components and the original
sources. To find out which ones are the components of
interest, we propose a criterion that allows to identify the
original sources. The excellent performance of kTDSEP is
demonstrated in experiments on nonlinearly mixed speech
data.
