Online group-sparse estimation using the covariance fitting criterion
(2017) 25th European Signal Processing Conference, EUSIPCO 2017 p.2101-2105- Abstract
In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/f092fe4e-b859-4a7b-9107-cc432c0d7a74
- author
- Kronvall, Ted LU ; Adalbjornsson, Stefan Ingi LU ; Nadig, Santhosh and Jakobsson, Andreas LU
- organization
- publishing date
- 2017-10-23
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Covariance fitting, Group sparsity, Multi-pitch estimation, Online estimation
- host publication
- 25th European Signal Processing Conference, EUSIPCO 2017
- article number
- 8081580
- pages
- 5 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 25th European Signal Processing Conference, EUSIPCO 2017
- conference location
- Kos, Greece
- conference dates
- 2017-08-28 - 2017-09-02
- external identifiers
-
- scopus:85041475534
- ISBN
- 9780992862671
- DOI
- 10.23919/EUSIPCO.2017.8081580
- language
- English
- LU publication?
- yes
- id
- f092fe4e-b859-4a7b-9107-cc432c0d7a74
- date added to LUP
- 2018-02-22 09:42:49
- date last changed
- 2022-02-15 01:06:08
@inproceedings{f092fe4e-b859-4a7b-9107-cc432c0d7a74, abstract = {{<p>In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved by reformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regularization level, for which a time-recursive implementation is derived. Using a proximal gradient step for lowering the computational cost, the proposed method may effectively cope with data sequences consisting of both stationary and non-stationary signals, such as transients, and/or amplitude modulated signals. Numerical examples illustrates the efficacy of the proposed method for both coherent Gaussian dictionaries and for the multi-pitch estimation problem.</p>}}, author = {{Kronvall, Ted and Adalbjornsson, Stefan Ingi and Nadig, Santhosh and Jakobsson, Andreas}}, booktitle = {{25th European Signal Processing Conference, EUSIPCO 2017}}, isbn = {{9780992862671}}, keywords = {{Covariance fitting; Group sparsity; Multi-pitch estimation; Online estimation}}, language = {{eng}}, month = {{10}}, pages = {{2101--2105}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Online group-sparse estimation using the covariance fitting criterion}}, url = {{http://dx.doi.org/10.23919/EUSIPCO.2017.8081580}}, doi = {{10.23919/EUSIPCO.2017.8081580}}, year = {{2017}}, }