Estimating sparse signals using integrated wide-band dictionaries
(2017) 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 p.4426-4430- Abstract
In this paper, we present a technique for reducing the size of the dictionary in sparse signal reconstruction by formulating an initial dictionary containing elements that spans bands of the considered parameter space. We allow for the use of this banded dictionary in a first-stage estimation procedure, in which large parts of the parameter space is discarded for further analysis, thereby reducing the overall computationally complexity required to allow for a reliable signal reconstruction. We illustrate the presented principle on the problem of estimating sinusoidal components corrupted by white noise.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2335ac96-9500-4638-ab8b-f376109f6654
- author
- Butsenko, Maksim
; Sward, Johan
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017-06-16
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- convex optimization, dictionary learning, Sparse signal reconstruction
- host publication
- 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
- article number
- 7952993
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
- conference location
- New Orleans, United States
- conference dates
- 2017-03-05 - 2017-03-09
- external identifiers
-
- scopus:85023748591
- ISBN
- 9781509041176
- DOI
- 10.1109/ICASSP.2017.7952993
- language
- English
- LU publication?
- yes
- id
- 2335ac96-9500-4638-ab8b-f376109f6654
- date added to LUP
- 2017-07-27 14:07:47
- date last changed
- 2022-04-25 01:38:38
@inproceedings{2335ac96-9500-4638-ab8b-f376109f6654, abstract = {{<p>In this paper, we present a technique for reducing the size of the dictionary in sparse signal reconstruction by formulating an initial dictionary containing elements that spans bands of the considered parameter space. We allow for the use of this banded dictionary in a first-stage estimation procedure, in which large parts of the parameter space is discarded for further analysis, thereby reducing the overall computationally complexity required to allow for a reliable signal reconstruction. We illustrate the presented principle on the problem of estimating sinusoidal components corrupted by white noise.</p>}}, author = {{Butsenko, Maksim and Sward, Johan and Jakobsson, Andreas}}, booktitle = {{2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings}}, isbn = {{9781509041176}}, keywords = {{convex optimization; dictionary learning; Sparse signal reconstruction}}, language = {{eng}}, month = {{06}}, pages = {{4426--4430}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Estimating sparse signals using integrated wide-band dictionaries}}, url = {{http://dx.doi.org/10.1109/ICASSP.2017.7952993}}, doi = {{10.1109/ICASSP.2017.7952993}}, year = {{2017}}, }