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Online Estimation of Multiple Harmonic Signals

Elvander, Filip LU ; Swärd, Johan LU and Jakobsson, Andreas LU (2017) In IEEE/ACM Transactions on Audio, Speech, and Language Processing 25(2). p.273-284
Abstract
In this paper, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least-squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. The amplitudes of the active pitches are also recursively updated, allowing for a smooth and more accurate representation. When evaluated on a set of ten music pieces, the proposed method is shown to outperform other general purpose multipitch estimators in either accuracy or computational speed,... (More)
In this paper, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least-squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. The amplitudes of the active pitches are also recursively updated, allowing for a smooth and more accurate representation. When evaluated on a set of ten music pieces, the proposed method is shown to outperform other general purpose multipitch estimators in either accuracy or computational speed, although not being able to yield performance as good as the state-of-the art methods, which are being optimally tuned and specifically trained on the present instruments. However, the method is able to outperform such a technique when used without optimal tuning, or when applied to instruments not included in the training data. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
adaptive signal processing, dictionary learning, group sparsity, multi-pitch estimation, sparse recursive least squares
in
IEEE/ACM Transactions on Audio, Speech, and Language Processing
volume
25
issue
2
pages
12 pages
publisher
Piscataway, NJ : Institute of Electrical and Electronics Engineers
external identifiers
  • scopus:85009876521
  • wos:000395551300005
ISSN
2329-9290
DOI
10.1109/TASLP.2016.2634118
language
English
LU publication?
yes
id
b5eca48e-2acd-437e-aebd-a14e1c468790
date added to LUP
2016-12-27 11:21:35
date last changed
2018-03-11 04:35:59
@article{b5eca48e-2acd-437e-aebd-a14e1c468790,
  abstract     = {In this paper, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least-squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. The amplitudes of the active pitches are also recursively updated, allowing for a smooth and more accurate representation. When evaluated on a set of ten music pieces, the proposed method is shown to outperform other general purpose multipitch estimators in either accuracy or computational speed, although not being able to yield performance as good as the state-of-the art methods, which are being optimally tuned and specifically trained on the present instruments. However, the method is able to outperform such a technique when used without optimal tuning, or when applied to instruments not included in the training data.},
  author       = {Elvander, Filip and Swärd, Johan and Jakobsson, Andreas},
  issn         = {2329-9290},
  keyword      = {adaptive signal processing,dictionary learning,group sparsity,multi-pitch estimation,sparse recursive least squares},
  language     = {eng},
  number       = {2},
  pages        = {273--284},
  publisher    = {Piscataway, NJ : Institute of Electrical and Electronics Engineers},
  series       = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
  title        = {Online Estimation of Multiple Harmonic Signals},
  url          = {http://dx.doi.org/10.1109/TASLP.2016.2634118},
  volume       = {25},
  year         = {2017},
}