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Multi-Pitch Estimation and Tracking Using Bayesian Inference in Block Sparsity

Karimian-Azari, Sam; Jakobsson, Andreas LU ; Jensen, Jesper and Christensen, Mads (2015) 23rd European Signal Processing Conference, 2015 In Signal Processing Conference (EUSIPCO), 2015 23rd European p.16-20
Abstract
n this paper, we consider the problem of multi-pitch estimation and tracking of an unknown number of harmonic audio sources. The regularized least-squares is a solution for simultaneous sparse source selection and parameter estimation. Exploiting block sparsity, the method allows for reliable tracking of the found sources, without posing detailed a priori assumptions of the number of harmonics for each source. The method incorporates a Bayesian prior and assigns data-dependent reg-ularization coefficients to efficiently incorporate both earlier and future data blocks in the tracking of estimates. In comparison with fix regularization coefficients, the simulation results, using both real and synthetic audio signals, confirm the performance... (More)
n this paper, we consider the problem of multi-pitch estimation and tracking of an unknown number of harmonic audio sources. The regularized least-squares is a solution for simultaneous sparse source selection and parameter estimation. Exploiting block sparsity, the method allows for reliable tracking of the found sources, without posing detailed a priori assumptions of the number of harmonics for each source. The method incorporates a Bayesian prior and assigns data-dependent reg-ularization coefficients to efficiently incorporate both earlier and future data blocks in the tracking of estimates. In comparison with fix regularization coefficients, the simulation results, using both real and synthetic audio signals, confirm the performance of the proposed method. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Signal Processing Conference (EUSIPCO), 2015 23rd European
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
23rd European Signal Processing Conference, 2015
external identifiers
  • Scopus:84963963425
DOI
10.1109/EUSIPCO.2015.7362336
language
English
LU publication?
yes
id
8fbe0359-3171-41ed-aeae-9b843278e370 (old id 7767441)
date added to LUP
2016-01-15 18:34:09
date last changed
2017-02-05 04:39:37
@inproceedings{8fbe0359-3171-41ed-aeae-9b843278e370,
  abstract     = {n this paper, we consider the problem of multi-pitch estimation and tracking of an unknown number of harmonic audio sources. The regularized least-squares is a solution for simultaneous sparse source selection and parameter estimation. Exploiting block sparsity, the method allows for reliable tracking of the found sources, without posing detailed a priori assumptions of the number of harmonics for each source. The method incorporates a Bayesian prior and assigns data-dependent reg-ularization coefficients to efficiently incorporate both earlier and future data blocks in the tracking of estimates. In comparison with fix regularization coefficients, the simulation results, using both real and synthetic audio signals, confirm the performance of the proposed method.},
  author       = {Karimian-Azari, Sam and Jakobsson, Andreas and Jensen, Jesper and Christensen, Mads},
  booktitle    = {Signal Processing Conference (EUSIPCO), 2015 23rd European},
  language     = {eng},
  pages        = {16--20},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Multi-Pitch Estimation and Tracking Using Bayesian Inference in Block Sparsity},
  url          = {http://dx.doi.org/10.1109/EUSIPCO.2015.7362336},
  year         = {2015},
}