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An Adaptive Penalty Multi-Pitch Estimator with Self-Regularization

Elvander, Filip LU ; Kronvall, Ted LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU (2016) In Signal Processing 127. p.56-70
Abstract
This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half the true fundamental frequency, the sub-octave, is chosen instead of the true pitch. Extending on current group LASSO-based methods for pitch estimation, this work introduces an adaptive total variation penalty, which enforces both group- and block sparsity, as well as deals with errors due to sub-octaves. Also presented is a scheme for signal adaptive dictionary construction and automatic selection of the regularization parameters. Used together with this scheme, the proposed method is shown to yield accurate pitch estimates when evaluated on synthetic speech data. The method is shown to perform as good as, or better... (More)
This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half the true fundamental frequency, the sub-octave, is chosen instead of the true pitch. Extending on current group LASSO-based methods for pitch estimation, this work introduces an adaptive total variation penalty, which enforces both group- and block sparsity, as well as deals with errors due to sub-octaves. Also presented is a scheme for signal adaptive dictionary construction and automatic selection of the regularization parameters. Used together with this scheme, the proposed method is shown to yield accurate pitch estimates when evaluated on synthetic speech data. The method is shown to perform as good as, or better than, current state-of-the-art sparse methods while requiring fewer tuning parameters than these, as well as several con- ventional pitch estimation methods, even when these are given oracle model orders. When evaluated on a set of ten musical pieces, the method shows promising results for separating multi-pitch signals. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Multi-pitch estimation, block sparsity, adaptive sparse penalty, self-regularization, ADMM
in
Signal Processing
volume
127
pages
56 - 70
publisher
Elsevier
external identifiers
  • scopus:84961214480
  • wos:000377325400006
ISSN
0165-1684
DOI
10.1016/j.sigpro.2016.02.015
language
English
LU publication?
yes
id
8237a0fe-8f52-4404-b201-fcf4d2bf7c75 (old id 8864026)
date added to LUP
2016-03-21 09:45:45
date last changed
2017-07-30 03:17:49
@article{8237a0fe-8f52-4404-b201-fcf4d2bf7c75,
  abstract     = {This work treats multi-pitch estimation, and in particular the common misclassification issue wherein the pitch at half the true fundamental frequency, the sub-octave, is chosen instead of the true pitch. Extending on current group LASSO-based methods for pitch estimation, this work introduces an adaptive total variation penalty, which enforces both group- and block sparsity, as well as deals with errors due to sub-octaves. Also presented is a scheme for signal adaptive dictionary construction and automatic selection of the regularization parameters. Used together with this scheme, the proposed method is shown to yield accurate pitch estimates when evaluated on synthetic speech data. The method is shown to perform as good as, or better than, current state-of-the-art sparse methods while requiring fewer tuning parameters than these, as well as several con- ventional pitch estimation methods, even when these are given oracle model orders. When evaluated on a set of ten musical pieces, the method shows promising results for separating multi-pitch signals.},
  author       = {Elvander, Filip and Kronvall, Ted and Adalbjörnsson, Stefan Ingi and Jakobsson, Andreas},
  issn         = {0165-1684},
  keyword      = {Multi-pitch estimation,block sparsity,adaptive sparse penalty,self-regularization,ADMM},
  language     = {eng},
  pages        = {56--70},
  publisher    = {Elsevier},
  series       = {Signal Processing},
  title        = {An Adaptive Penalty Multi-Pitch Estimator with Self-Regularization},
  url          = {http://dx.doi.org/10.1016/j.sigpro.2016.02.015},
  volume       = {127},
  year         = {2016},
}