Multi-pitch estimation via fast group sparse learning
(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:
https://lup.lub.lu.se/record/b0b4b62c-08e2-42c4-9ed8-5ed83dd61c3d
- author
- Kronvall, Ted LU ; Elvander, Filip LU ; Adalbjörnsson, Stefan Ingi LU and Jakobsson, Andreas LU
- organization
- publishing date
- 2016-12-01
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2016 24th European Signal Processing Conference (EUSIPCO)
- series title
- 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
- conference location
- Budapest, Hungary
- conference dates
- 2016-08-28 - 2016-09-02
- 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
- 2022-02-21 22:21:40
@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 = {{2016 24th 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.}}, series = {{European Signal Processing Conference (EUSIPCO)}}, title = {{Multi-pitch estimation via fast group sparse learning}}, url = {{http://dx.doi.org/10.1109/EUSIPCO.2016.7760417}}, doi = {{10.1109/EUSIPCO.2016.7760417}}, year = {{2016}}, }