Estimating sparse signals using integrated wide-band dictionaries

Maksim Butsenko; Johan Sward; Andreas Jakobsson

doi:10.1109/ICASSP.2017.7952993

Estimating sparse signals using integrated wide-band dictionaries

Maksim Butsenko, Johan Sward, Andreas Jakobsson

Statistical Signal Processing Group

Tallinn University of Technology

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

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.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	4426-4430
Number of pages	5
ISBN (Electronic)	9781509041176
DOIs	https://doi.org/10.1109/ICASSP.2017.7952993
Publication status	Published - 2017 Jun 16
Event	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States Duration: 2017 Mar 5 → 2017 Mar 9

Conference

Conference	2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	2017/03/05 → 2017/03/09

Subject classification (UKÄ)

Signal Processing

Free keywords

convex optimization
dictionary learning
Sparse signal reconstruction

Access to Document

10.1109/ICASSP.2017.7952993

Cite this

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title = "Estimating sparse signals using integrated wide-band dictionaries",

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.",

keywords = "convex optimization, dictionary learning, Sparse signal reconstruction",

author = "Maksim Butsenko and Johan Sward and Andreas Jakobsson",

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doi = "10.1109/ICASSP.2017.7952993",

language = "English",

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booktitle = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

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note = "2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",

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Butsenko, M, Sward, J & Jakobsson, A 2017, Estimating sparse signals using integrated wide-band dictionaries. in 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings., 7952993, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 4426-4430, 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, New Orleans, United States, 2017/03/05. https://doi.org/10.1109/ICASSP.2017.7952993

Estimating sparse signals using integrated wide-band dictionaries. / Butsenko, Maksim; Sward, Johan; Jakobsson, Andreas.
2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. p. 4426-4430 7952993.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

T1 - Estimating sparse signals using integrated wide-band dictionaries

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AU - Sward, Johan

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N2 - 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.

AB - 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.

KW - convex optimization

KW - dictionary learning

KW - Sparse signal reconstruction

U2 - 10.1109/ICASSP.2017.7952993

DO - 10.1109/ICASSP.2017.7952993

M3 - Paper in conference proceeding

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SP - 4426

EP - 4430

BT - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017

Y2 - 5 March 2017 through 9 March 2017

ER -

Estimating sparse signals using integrated wide-band dictionaries

Abstract

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Subject classification (UKÄ)

Free keywords

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