Fast and effective model order selection method to determine the number of sources in a linear transformation model

Fengyu Cong, Asoke K. Nandi, Zhaoshui He, Andrzej Cichocki, Tapani Ristaniemi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Citations (Scopus)

Abstract

This paper formally introduces the method named as RAE (ratio of adjacent eigenvalues) for model order selection, and proposes a new approach combining the recently developed SORTE (Second ORder sTatistic of the Eigenvalues) and RAE in the context for determining the number of sources in a linear transformation model. The underlying rationale for the combination discovered through sufficient simulations is that SORTE overestimated the true order in the model and RAE underestimated the true order when the signal to noise ratio (SNR) was low. Simulations further showed that after the new method, called RAESORTE, was optimized, the true number of sources was almost correctly estimated even when the SNR was 10 dB, which is extremely difficult for any other model order selection methods; moreover, RAE took much less time than SORTE known as computational efficiency. Hence, RAE and RAESORTE appear promising for the real-time and real world signal processing.

Original languageEnglish
Title of host publicationProceedings of the 20th European Signal Processing Conference, EUSIPCO 2012
Pages1870-1874
Number of pages5
Publication statusPublished - 2012
Externally publishedYes
Event20th European Signal Processing Conference, EUSIPCO 2012 - Bucharest, Romania
Duration: 27 Aug 201231 Aug 2012

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491

Conference

Conference20th European Signal Processing Conference, EUSIPCO 2012
Country/TerritoryRomania
CityBucharest
Period27/08/1231/08/12

Keywords

  • Linear transformation model
  • model order selection
  • number of sources
  • ratio of adjacent eigenvalues
  • signal to noise ratio

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