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Online group-sparse estimation using the covariance fitting criterion

Kronvall, Ted LU ; Adalbjornsson, Stefan Ingi LU ; Nadig, Santhosh and Jakobsson, Andreas LU (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.

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author
organization
publishing date
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
pages
5 pages
publisher
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
2019-03-08 02:34:17
@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},
  isbn         = {9780992862671},
  keyword      = {Covariance fitting,Group sparsity,Multi-pitch estimation,Online estimation},
  language     = {eng},
  location     = {Kos, Greece},
  month        = {10},
  pages        = {2101--2105},
  publisher    = {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},
  year         = {2017},
}