Geometrical understanding of the PCA subspace method for overdetermined blind source separation

Stefan Winter, Hiroshi Sawada, Shoji Makino

Research output: Contribution to journalConference articlepeer-review

9 Citations (Scopus)

Abstract

In this paper, we discuss approaches for blind source separation where we can use more sensors than the number of sources for a better performance. The discussion focuses mainly on reducing the dimension 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 involves selecting a subset of sensors based on 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, which provides a better understanding of the former method.

Original languageEnglish
Pages (from-to)769-772
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
Publication statusPublished - 2003
Externally publishedYes
Event2003 IEEE International Conference on Accoustics, Speech, and Signal Processing - Hong Kong, Hong Kong
Duration: 2003 Apr 62003 Apr 10

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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