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Viewing report
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A New Approach for Blind Source Separation of
Convolutive Sources. Edition No. 1
VDM Publishing House, Sep 2008, Pages: 84
ICA and its variations are used extensively in BSS.
Most of the algorithms that are used to separate
speech or music signals utilize ICA in the time
frequency domain. Here ICA is applied in the wavelet
domain. Separation of signals is achieved by
applying the ICA algorithm and shrinkage functions
to the wavelet coefficients of the original
mixtures. ICA alone can achieve reasonably good
separation of artificially convolved sources;
however, poor separation quality is experienced for
real world convolutive mixtures. This work presents
a novel post processing technique to deal with the
cross talk problem. The post processor is applied to
the signals separated by the ICA network. A super
Gaussian form of the PDF is assumed for the dominant
source components. Closed form solutions of the
parameters of the PDF are obtained by the MOM. The
PDF of the cross talk components is assumed to be of
a GMM, and the EM method is applied to determine the
parameters of the Gaussian mixtures. The algorithm
is applied to a real world mixture of music and
speech signals. The results show a significant
reduction in the cross talk.
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