DOA estimation for multiple sparse sources with normalized observation vector clustering

Shoko Araki, Hiroshi Sawada, Ryo Mukai, Shoji Makino

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

63 Citations (Scopus)

Abstract

This paper presents a new method for estimating the direction of arrival (DOA) of source signals whose number N can exceed the number of sensors M. Subspace based methods, e.g., the MUSIC algorithm, have been widely studied, however, they are only applicable when M > N. Another conventional independent component analysis based method allows M > N, however, it cannot be applied when M < N. By contrast, our new method can be applied where the sources outnumber the sensors (i.e., an underdetermined case M < N) by assuming source sparseness. Our method can cope with 2- or 3-dimensionally distributed sources with a 2- or 3-dimensional sensor array. We obtained promising experimental results for 3 × 4, 3 × 5 and 4×5 (#sensors × #speech sources) in a room (RT60 = 120 ms).

Original languageEnglish
Title of host publication2006 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings
PagesV33-V36
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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