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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 orcid (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:
author
; ; and
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
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}},
}