Subspace-Based Estimation of Symbolic Periodicities

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Abstract

In this work, we propose a novel subspace-based estimator of periodicities in symbolic sequences. The estimator exploits the harmonic structure naturally occurring in symbolic se- quences and iteratively forms the estimate of the periodicities using a MUSIC-like formulation. The estimator allows for alphabets of different sizes, but is here illustrated using both simulated and real DNA measurements, showing a notable performance gain as compared to other common estimators.
Original languageEnglish
Title of host publication[Host publication title missing]
PublisherIEEE - Institute of Electrical and Electronics Engineers Inc.
Pages6210-6214
Publication statusPublished - 2013
EventThe 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) - Vancouver, Canada
Duration: 2013 May 262013 May 31

Publication series

Name
ISSN (Print)1520-6149

Conference

ConferenceThe 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
Country/TerritoryCanada
CityVancouver
Period2013/05/262013/05/31

Bibliographical note

The paper is to appear in the IEEE conference proceedings
"Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on"

Subject classification (UKÄ)

  • Probability Theory and Statistics

Keywords

  • Spectrum analysis
  • symbolic sequences
  • hidden periodicities
  • subspace techniques.

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