Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ted Kronvall"'
Autor:
Andreas Jakobsson, Ted Kronvall
Publikováno v:
Signal Processing; 151, pp 107-118 (2018)
This work analyzes the effects on support recovery for different choices of the hyper- or regularization parameter in LASSO-like sparse and group-sparse regression problems. The hyperparameter implicitly selects the model order of the solution, and i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d05e8ecba79e52468bc10d0da8a379fa
https://doi.org/10.1016/j.sigpro.2018.04.021
https://doi.org/10.1016/j.sigpro.2018.04.021
Publikováno v:
Signal Processing. 139:116-130
A generalization of the covariance fitting criteria, for grouped variables, is presented.An hyperparameter-free analogue to the SPICE method is proposed for grouped variables, termed group-SPICE.The connection between group-SPICE and the group-LASSO
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3591608fd70b06f20ebdb709fbe11c4c
https://doi.org/10.1016/j.sigpro.2017.03.025
https://doi.org/10.1016/j.sigpro.2017.03.025
Publikováno v:
Signal Processing. 130:105-117
This work treats the estimation of chroma features for harmonic audio signals using a sparse reconstruction framework. Chroma has been used for decades as a key tool in audio analysis, and is typically formed using a periodogram-based approach that m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::56a7fb7b80e93d51d500f4a536ff3ab0
https://doi.org/10.1016/j.sigpro.2016.06.020
https://doi.org/10.1016/j.sigpro.2016.06.020
Publikováno v:
Signal Processing. 127:56-70
This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half the true fundamental frequency, the sub-octave, is chosen instead of the true pitch. Extending on current group LASSO-based method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdcafa695ec5cde372859d075780b142
https://doi.org/10.1016/j.sigpro.2016.02.015
https://doi.org/10.1016/j.sigpro.2016.02.015
Publikováno v:
IEEE/ACM Transactions on Audio, Speech, and Language Processing; 24(1), pp 117-129 (2016)
In this paper, we propose a novel method for estimating the locations of near- and/or far-field harmonic audio sources impinging on an arbitrary, but calibrated, sensor array. Using a joint pitch and location estimation formed in two steps, we first
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0bf3f58c9f00d63fcb1eff3260085639
https://doi.org/10.1109/taslp.2015.2497798
https://doi.org/10.1109/taslp.2015.2497798
Autor:
Andreas Jakobsson, Ted Kronvall
Publikováno v:
ACSSC
The choice of hyperparameter(s) notably affects the support recovery in LASSO-like sparse regression problems, acting as an implicit model order selection. Parameters are typically selected using cross-validation or various ad hoc approaches. These o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe1aee8032a587e46b8b61d757166ec9
https://doi.org/10.1109/acssc.2017.8335469
https://doi.org/10.1109/acssc.2017.8335469
Publikováno v:
EUSIPCO
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 regul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::268276a20105b4dece5f7629b044eb81
https://doi.org/10.23919/eusipco.2017.8081580
https://doi.org/10.23919/eusipco.2017.8081580
Publikováno v:
European Signal Processing Conference (EUSIPCO); CFP1740S-USB, no 1570347373 (2017)
Lund University
Lund University
In this paper, we present a time-recursive implementation of a recent hyperparameter-free group-sparse estimation technique. This is achieved byr eformulating the original method, termed group-SPICE, as a square-root group-LASSO with a suitable regul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::cb3fb28023d1f6ddbdbc54ccea59f23c
https://lup.lub.lu.se/record/40f6dfcc-75fe-4384-931d-d17494c3d55e
https://lup.lub.lu.se/record/40f6dfcc-75fe-4384-931d-d17494c3d55e
Publikováno v:
ACSSC
In this paper, we introduce a novel framework for semi-parametric estimation of an unknown number of signals, each parametrized by a group of components. Via a reformulation of the covariance fitting criteria, we formulate a convex optimization probl
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c6d91246362c6b13987684499392759a
https://doi.org/10.1109/acssc.2016.7869067
https://doi.org/10.1109/acssc.2016.7869067
Publikováno v:
EUSIPCO
In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::74f3ac1f2e5974571a24964421b0e873
https://doi.org/10.1109/eusipco.2016.7760417
https://doi.org/10.1109/eusipco.2016.7760417