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Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares

Elvander, Filip LU ; Swärd, Johan LU and Jakobsson, Andreas LU (2017) 50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016) In 50th Asilomar Conference on Signals, Systems, and Computers, 2016 p.369-373
Abstract
In this work, we propose a time-recursive multi-pitch estimation algorithm, using a sparse reconstruction framework, assuming
only a few pitches from a large set of candidates to be active at each time instant. The proposed algorithm utilizes a sparse
recursive least squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on
the solution. When evaluated on a set of ten music pieces, the proposed method is shown to outperform state-of-the-art multi-
pitch estimators in either accuracy or computational spe
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
50th Asilomar Conference on Signals, Systems, and Computers, 2016
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016)
external identifiers
  • scopus:85016336731
ISBN
978-1-5386-3954-2
DOI
10.1109/ACSSC.2016.7869062
language
English
LU publication?
yes
id
967ec803-d5a2-4c4a-a4c1-70e6c2d42457
date added to LUP
2016-09-22 18:53:12
date last changed
2018-01-07 11:27:50
@inproceedings{967ec803-d5a2-4c4a-a4c1-70e6c2d42457,
  abstract     = {In this work, we propose a time-recursive multi-pitch estimation algorithm, using a sparse reconstruction framework, assuming <br/>only a few pitches from a large set of candidates to be active at each time instant. The proposed algorithm utilizes a sparse <br/>recursive least squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on <br/>the solution. When evaluated on a set of ten music pieces, the proposed method is shown to outperform state-of-the-art multi-<br/>pitch estimators in either accuracy or computational spe},
  author       = {Elvander, Filip and Swärd, Johan and Jakobsson, Andreas},
  booktitle    = {50th Asilomar Conference on Signals, Systems, and Computers, 2016 },
  isbn         = {978-1-5386-3954-2},
  language     = {eng},
  pages        = {369--373},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares},
  url          = {http://dx.doi.org/10.1109/ACSSC.2016.7869062},
  year         = {2017},
}