Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
410985 | Neurocomputing | 2006 | 13 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hyung-Min Park, Chandra Shekhar Dhir, Sang-Hoon Oh, Soo-Young Lee,