On feature combination for music classification

Zhouyu Fu; Guojun Lu; Kai-Ming Ting; Dengsheng Zhang

doi:10.1007/978-3-642-14980-1_44

On feature combination for music classification

Zhouyu Fu, Guojun Lu, Kai-Ming Ting, Dengsheng Zhang

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

10 Citations (Scopus)

Abstract

![CDATA[We address the problem of combining different types of audio features for music classification. Several feature-level and decision-level combination methods have been studied, including kernel methods based on multiple kernel learning, decision level fusion rules and stacked generalization. Eight widely used audio features were examined in the experiments on multi-feature based music classification. Results on benchmark data set have demonstrated the effectiveness of using multiple types of features for music classification and identified the most effective combination method for improving classification performance.]]

Original language	English
Title of host publication	Structural, Syntactic, and Statistical Pattern Recognition: Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, 18-20 August 2010
Publisher	Springer
Pages	453-462
Number of pages	10
ISBN (Print)	9783642149795
DOIs	https://doi.org/10.1007/978-3-642-14980-1_44
Publication status	Published - 2010
Event	International Workshop on Structural_Syntactic and Statistical Pattern Recognition - Duration: 18 Aug 2010 → …

Conference

Conference	International Workshop on Structural_Syntactic and Statistical Pattern Recognition
Period	18/08/10 → …

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10.1007/978-3-642-14980-1_44

Cite this

@inproceedings{9e5b60c76b0a432ca3f5787e9cb96bec,

title = "On feature combination for music classification",

abstract = "![CDATA[We address the problem of combining different types of audio features for music classification. Several feature-level and decision-level combination methods have been studied, including kernel methods based on multiple kernel learning, decision level fusion rules and stacked generalization. Eight widely used audio features were examined in the experiments on multi-feature based music classification. Results on benchmark data set have demonstrated the effectiveness of using multiple types of features for music classification and identified the most effective combination method for improving classification performance.]]",

author = "Zhouyu Fu and Guojun Lu and Kai-Ming Ting and Dengsheng Zhang",

year = "2010",

doi = "10.1007/978-3-642-14980-1_44",

language = "English",

isbn = "9783642149795",

pages = "453--462",

booktitle = "Structural, Syntactic, and Statistical Pattern Recognition: Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, 18-20 August 2010",

publisher = "Springer",

note = "International Workshop on Structural_Syntactic and Statistical Pattern Recognition ; Conference date: 18-08-2010",

}

Fu, Z, Lu, G, Ting, K-M & Zhang, D 2010, On feature combination for music classification. in Structural, Syntactic, and Statistical Pattern Recognition: Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, 18-20 August 2010. Springer, pp. 453-462, International Workshop on Structural_Syntactic and Statistical Pattern Recognition, 18/08/10. https://doi.org/10.1007/978-3-642-14980-1_44

On feature combination for music classification. / Fu, Zhouyu; Lu, Guojun; Ting, Kai-Ming et al.
Structural, Syntactic, and Statistical Pattern Recognition: Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, 18-20 August 2010. Springer, 2010. p. 453-462.

Research output: Chapter in Book / Conference Paper › Conference Paper › peer-review

TY - GEN

T1 - On feature combination for music classification

AU - Fu, Zhouyu

AU - Lu, Guojun

AU - Ting, Kai-Ming

AU - Zhang, Dengsheng

PY - 2010

Y1 - 2010

N2 - ![CDATA[We address the problem of combining different types of audio features for music classification. Several feature-level and decision-level combination methods have been studied, including kernel methods based on multiple kernel learning, decision level fusion rules and stacked generalization. Eight widely used audio features were examined in the experiments on multi-feature based music classification. Results on benchmark data set have demonstrated the effectiveness of using multiple types of features for music classification and identified the most effective combination method for improving classification performance.]]

AB - ![CDATA[We address the problem of combining different types of audio features for music classification. Several feature-level and decision-level combination methods have been studied, including kernel methods based on multiple kernel learning, decision level fusion rules and stacked generalization. Eight widely used audio features were examined in the experiments on multi-feature based music classification. Results on benchmark data set have demonstrated the effectiveness of using multiple types of features for music classification and identified the most effective combination method for improving classification performance.]]

UR - http://handle.uws.edu.au:8081/1959.7/562984

UR - http://www.rvg.ua.es/ssspr2010/

U2 - 10.1007/978-3-642-14980-1_44

DO - 10.1007/978-3-642-14980-1_44

M3 - Conference Paper

SN - 9783642149795

SP - 453

EP - 462

BT - Structural, Syntactic, and Statistical Pattern Recognition: Proceedings of the Joint IAPR International Workshop, SSPR & SPR 2010, Cesme, Izmir, Turkey, 18-20 August 2010

PB - Springer

T2 - International Workshop on Structural_Syntactic and Statistical Pattern Recognition

Y2 - 18 August 2010

ER -

On feature combination for music classification

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