Geometrical interpretation of the PCA subspace approach for overdetermined blind source separation

Authors:
S. Winter;H. Sawada;S. Makino
Affiliations:
NTT Communication Science Laboratories, NTT Corporation, Hikaridai Seika-cho, Soraku-gun, Kyoto, Japan and Department of Multimedia Communication and Signal Processing, University of Erlangen-Nure ...;NTT Communication Science Laboratories, NTT Corporation, Hikaridai Seika-cho, Soraku-gun, Kyoto, Japan;NTT Communication Science Laboratories, NTT Corporation, Hikaridai Seika-cho, Soraku-gun, Kyoto, Japan
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2006

Citing 2
Cited 2

Blind source separation in the presence of weak sources

Neural Networks
Exploring permutation inconsistency in blind separation of speech signals in a reverberant environment

ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02

Map-based underdetermined blind source separation of convolutive mixtures by hierarchical clustering and l1-norm minimization

EURASIP Journal on Applied Signal Processing
Bayesian Nonparametrics for Microphone Array Processing

IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We discuss approaches for blind source separation where we can use more sensors than sources to obtain a better performance. The discussion focuses mainly on reducing the dimensions of mixed signals before applying independent component analysis. We compare two previously proposed methods. The first is based on principal component analysis, where noise reduction is achieved. The second is based on geometric considerations and selects a subset of sensors in accordance with the fact that a low frequency prefers a wide spacing, and a high frequency prefers a narrow spacing. We found that the PCA-based method behaves similarly to the geometry-based method for low frequencies in the way that it emphasizes the outer sensors and yields superior results for high frequencies. These results provide a better understanding of the former method.