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)
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:
https://lup.lub.lu.se/record/967ec803-d5a2-4c4a-a4c1-70e6c2d42457
- author
- Elvander, Filip
LU
; Swärd, Johan
LU
and Jakobsson, Andreas
LU
- organization
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 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)
- conference location
- Pacific Grove, United States
- conference dates
- 2016-11-06 - 2016-11-09
- 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
- 2022-03-08 21:01:05
@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}},
doi = {{10.1109/ACSSC.2016.7869062}},
year = {{2017}},
}