Subspace-Based Estimation of Symbolic Periodicities

Johan Swärd, Andreas Jakobsson

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

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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.
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

ISSN (Print)1520-6149


ConferenceThe 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)

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

Free keywords

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


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