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.
|Title of host publication||[Host publication title missing]|
|Publisher||IEEE - Institute of Electrical and Electronics Engineers Inc.|
|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
|Conference||The 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)|
|Period||2013/05/26 → 2013/05/31|
Bibliographical noteThe 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
- Spectrum analysis
- symbolic sequences
- hidden periodicities
- subspace techniques.