Multipitch estimation and instrument recognition by exemplar-based sparse representation

Ikuo Degawa; Kei Sato; Masaaki Ikehara

doi:10.1109/ACSSC.2013.6810341

Multipitch estimation and instrument recognition by exemplar-based sparse representation

Ikuo Degawa, Kei Sato, Masaaki Ikehara

Department of Electronics and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l₁ minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.

Original language	English
Title of host publication	Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	560-564
Number of pages	5
ISBN (Print)	9781479923908
DOIs	https://doi.org/10.1109/ACSSC.2013.6810341
Publication status	Published - 2013 Jan 1
Event	2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States Duration: 2013 Nov 3 → 2013 Nov 6

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	2013 47th Asilomar Conference on Signals, Systems and Computers
Country/Territory	United States
City	Pacific Grove, CA
Period	13/11/3 → 13/11/6

Keywords

instrument recognition
l1 regularized minimization
note exemplar
pitch estimation

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

Access to Document

10.1109/ACSSC.2013.6810341

Cite this

Degawa, I., Sato, K., & Ikehara, M. (2013). Multipitch estimation and instrument recognition by exemplar-based sparse representation. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 560-564). Article 6810341 (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810341

Multipitch estimation and instrument recognition by exemplar-based sparse representation. / Degawa, Ikuo; Sato, Kei; Ikehara, Masaaki.
Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers. IEEE Computer Society, 2013. p. 560-564 6810341 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Degawa, I, Sato, K & Ikehara, M 2013, Multipitch estimation and instrument recognition by exemplar-based sparse representation. in Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers., 6810341, Conference Record - Asilomar Conference on Signals, Systems and Computers, IEEE Computer Society, pp. 560-564, 2013 47th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, United States, 13/11/3. https://doi.org/10.1109/ACSSC.2013.6810341

@inproceedings{81821f5e76fc4f5396cac0a3533de581,

title = "Multipitch estimation and instrument recognition by exemplar-based sparse representation",

abstract = "This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l1 minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.",

keywords = "instrument recognition, l1 regularized minimization, note exemplar, pitch estimation",

author = "Ikuo Degawa and Kei Sato and Masaaki Ikehara",

year = "2013",

month = jan,

day = "1",

doi = "10.1109/ACSSC.2013.6810341",

language = "English",

isbn = "9781479923908",

series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",

publisher = "IEEE Computer Society",

pages = "560--564",

booktitle = "Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers",

note = "2013 47th Asilomar Conference on Signals, Systems and Computers ; Conference date: 03-11-2013 Through 06-11-2013",

}

TY - GEN

T1 - Multipitch estimation and instrument recognition by exemplar-based sparse representation

AU - Degawa, Ikuo

AU - Sato, Kei

AU - Ikehara, Masaaki

PY - 2013/1/1

Y1 - 2013/1/1

N2 - This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l1 minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.

AB - This paper investigates the pitch estimation and the instrument recognition of music signals. A note exemplar is a spectrum segment of notes of the specific pitch and instrument, which is stored as a form of dictionary preliminarily. We describe the method of reconstructing a frame of musical signals as the linear combination of exemplars from the large exemplar dictionary with sparse (l1 minimized) coefficient vector. Reconstruction constraints are imposed to KL divergence of spectra, which is found to produce better results than Euclidean distance. The proposed algorithm shows the ability to transcript music pieces with relatively many notes per a frame and to divide the instrument explicitly through some experiments.

KW - instrument recognition

KW - l1 regularized minimization

KW - note exemplar

KW - pitch estimation

UR - http://www.scopus.com/inward/record.url?scp=84901247544&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84901247544&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2013.6810341

DO - 10.1109/ACSSC.2013.6810341

M3 - Conference contribution

AN - SCOPUS:84901247544

SN - 9781479923908

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 560

EP - 564

BT - Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers

PB - IEEE Computer Society

T2 - 2013 47th Asilomar Conference on Signals, Systems and Computers

Y2 - 3 November 2013 through 6 November 2013

ER -

Multipitch estimation and instrument recognition by exemplar-based sparse representation

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this