A filter bank approach to independent component analysis for convolved mixtures

We present a filter bank approach to perform independent component analysis (ICA) for convolved mixtures. Input signals are split into subband signals and subsampled. A simplified network performs ICA on the subsampled signals, and finally independent components are synthesized. The proposed approach achieves superior performance than the frequency domain approach and faster convergence with less computational complexity than the time domain approach. Furthermore, it requires shorter unmixing filter length and less computational complexity than other filter bank approaches by designing efficient filter banks. Also, a method is proposed to resolve the permutation and scaling problems of the filter bank approach. (c) 2006 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2006-10
Language
ENG
Keywords

BLIND SIGNAL SEPARATION; SUBBANDS

Citation

NEUROCOMPUTING, v.69, pp.2065 - 2077

ISSN
0925-2312
URI
http://hdl.handle.net/10203/10216
Appears in Collection
EE-Journal Papers(저널논문)
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