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 |
|---|---|
| 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 | |
|---|---|
| ISSN (Print) | 1520-6149 |
Conference
| Conference | The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) |
|---|---|
| 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.