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Multi-pitch estimation via fast group sparse learning

Kronvall, Ted LU ; Elvander, Filip LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU (2016) 24th European Signal Processing Conference, EUSIPCO 2016 In European Signal Processing Conference (EUSIPCO) p.1093-1097
Abstract
In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belonging to other pitches, thereby causing ambiguity between pitches with similar frequency content. The problem is further complicated by the fact that the number of pitches is typically not known. In this work, we propose a sparse modeling framework using a generalized chroma representation in order to remove redundancy and lower the dictionary's block-coherency. The found chroma estimates are then used to solve a small convex problem, whereby spectral smoothness is enforced, resulting in the... (More)
In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belonging to other pitches, thereby causing ambiguity between pitches with similar frequency content. The problem is further complicated by the fact that the number of pitches is typically not known. In this work, we propose a sparse modeling framework using a generalized chroma representation in order to remove redundancy and lower the dictionary's block-coherency. The found chroma estimates are then used to solve a small convex problem, whereby spectral smoothness is enforced, resulting in the corresponding pitch estimates. Compared with previously published sparse approaches, the resulting algorithm reduces the computational complexity of each iteration, as well as speeding up the overall convergence. (Less)
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
European Signal Processing Conference (EUSIPCO)
pages
5 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
conference name
24th European Signal Processing Conference, EUSIPCO 2016
external identifiers
  • scopus:85006008598
ISSN
2076-1465
ISBN
978-0-9928-6265-7
DOI
10.1109/EUSIPCO.2016.7760417
language
English
LU publication?
yes
id
b0b4b62c-08e2-42c4-9ed8-5ed83dd61c3d
date added to LUP
2016-12-02 10:57:40
date last changed
2017-10-01 05:26:45
@inproceedings{b0b4b62c-08e2-42c4-9ed8-5ed83dd61c3d,
  abstract     = {In this work, we consider the problem of multi-pitch estimation using sparse heuristics and convex modeling. In general, this is a difficult non-linear optimization problem, as the frequencies belonging to one pitch often overlap the frequencies belonging to other pitches, thereby causing ambiguity between pitches with similar frequency content. The problem is further complicated by the fact that the number of pitches is typically not known. In this work, we propose a sparse modeling framework using a generalized chroma representation in order to remove redundancy and lower the dictionary's block-coherency. The found chroma estimates are then used to solve a small convex problem, whereby spectral smoothness is enforced, resulting in the corresponding pitch estimates. Compared with previously published sparse approaches, the resulting algorithm reduces the computational complexity of each iteration, as well as speeding up the overall convergence.},
  author       = {Kronvall, Ted and Elvander, Filip and Adalbjörnsson, Stefan Ingi and Jakobsson, Andreas},
  booktitle    = {European Signal Processing Conference (EUSIPCO)},
  isbn         = {978-0-9928-6265-7},
  issn         = {2076-1465},
  language     = {eng},
  month        = {12},
  pages        = {1093--1097},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  title        = {Multi-pitch estimation via fast group sparse learning},
  url          = {http://dx.doi.org/10.1109/EUSIPCO.2016.7760417},
  year         = {2016},
}