Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares

Filip Elvander; Johan Swärd; Andreas Jakobsson

doi:10.1109/ACSSC.2016.7869062

Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares

Filip Elvander, Johan Swärd, Andreas Jakobsson

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

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

Originalspråk	engelska
Titel på värdpublikation	50th Asilomar Conference on Signals, Systems, and Computers, 2016
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	369-373
Antal sidor	5
ISBN (elektroniskt)	978-1-5386-3954-2
DOI	https://doi.org/10.1109/ACSSC.2016.7869062
Status	Published - 2017
Evenemang	50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016) - Asilomar Hotel & Conference Grounds, Pacific Grove, USA Varaktighet: 2016 nov. 6 → 2016 nov. 9

Konferens

Konferens	50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016)
Land/Territorium	USA
Ort	Pacific Grove
Period	2016/11/06 → 2016/11/09

Ämnesklassifikation (UKÄ)

Sannolikhetsteori och statistik
Signalbehandling

Tillgång till dokument

10.1109/ACSSC.2016.7869062

Citera det här

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title = "Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares",

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",

author = "Filip Elvander and Johan Sw{\"a}rd and Andreas Jakobsson",

year = "2017",

doi = "10.1109/ACSSC.2016.7869062",

language = "English",

pages = "369--373",

booktitle = "50th Asilomar Conference on Signals, Systems, and Computers, 2016",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016) ; Conference date: 06-11-2016 Through 09-11-2016",

}

Elvander, F , Swärd, J & Jakobsson, A 2017, Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares. i 50th Asilomar Conference on Signals, Systems, and Computers, 2016 . IEEE - Institute of Electrical and Electronics Engineers Inc., s. 369-373, 50th Annual Asilomar Conference on Signals, Systems, and Computers (ASILOMAR 2016), Pacific Grove, USA, 2016/11/06. https://doi.org/10.1109/ACSSC.2016.7869062

Time Recursive Multi-Pitch Estimation Using Group Sparse Recursive Least Squares. / Elvander, Filip ; Swärd, Johan ; Jakobsson, Andreas.

50th Asilomar Conference on Signals, Systems, and Computers, 2016 . IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. s. 369-373.

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

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