Frequency domain blind source separation of a reduced amount of data using frequency normalization

Enrique Robledo-Arnuncio, Hiroshi Sawada, Shoji Makino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

The problem of blind source separation (BSS) from convolutive mixtures is often addressed using independent component analysis in the frequency domain. The separation performance with this approach degrades significantly when only a short amount of data is available, since the estimation of the separation system becomes inaccurate. In this paper we present a novel approach to the frequency domain BSS using frequency normalization. Under the conditions of almost sparse sources and of dominant direct path in the mixing systems, we show that the new approach provides better performance than the conventional one when the amount of available data is small.

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesV837-V840
Publication statusPublished - 2006
Externally publishedYes
Event2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006 - Toulouse, France
Duration: 2006 May 142006 May 19

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume5
ISSN (Print)1520-6149

Conference

Conference2006 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2006
CountryFrance
CityToulouse
Period06/5/1406/5/19

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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