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 language | English |
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Title of host publication | [Host publication title missing] |
Publisher | IEEE - Institute of Electrical and Electronics Engineers Inc. |
Pages | 6210-6214 |
Publication status | Published - 2013 |
Event | The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) - Vancouver, Canada Duration: 2013 May 26 → 2013 May 31 |
Publication series
Name | |
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ISSN (Print) | 1520-6149 |
Conference
Conference | The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) |
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Country/Territory | Canada |
City | Vancouver |
Period | 2013/05/26 → 2013/05/31 |
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
Free keywords
- Spectrum analysis
- symbolic sequences
- hidden periodicities
- subspace techniques.