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
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7767441
- author
- Karimian-Azari, Sam
; Jakobsson, Andreas
LU
; Jensen, Jesper
and Christensen, Mads
- organization
- publishing date
- 2015
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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
- conference location
- Nice, France
- conference dates
- 2015-08-31 - 2015-09-04
- 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
- 2016-04-04 10:18:52
- date last changed
- 2022-01-29 20:04:34
@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 = {{https://lup.lub.lu.se/search/files/5509980/7767442.pdf}},
doi = {{10.1109/EUSIPCO.2015.7362336}},
year = {{2015}},
}