Musical data mining for electronic music distribution

Psychology

Text available via DOI:

https://doi.org/10.1109/WDM.2001.990164
Final published version

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Musical data mining for electronic music distribution. / Pachet, F ; Westermann, G ; Laigre, D .
First International Conference on Web Delivering of Music, 2001. Proceedings. . ed. / P Nesi; P Bellini; C Busch. LOS ALAMITOS: IEEE COMPUTER SOC, 2001. p. 101-106.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Pachet, F, Westermann, G & Laigre, D 2001, Musical data mining for electronic music distribution. in P Nesi, P Bellini & C Busch (eds), First International Conference on Web Delivering of Music, 2001. Proceedings. . IEEE COMPUTER SOC, LOS ALAMITOS, pp. 101-106. https://doi.org/10.1109/WDM.2001.990164

APA

Pachet, F., Westermann, G., & Laigre, D. (2001). Musical data mining for electronic music distribution. In P. Nesi, P. Bellini, & C. Busch (Eds.), First International Conference on Web Delivering of Music, 2001. Proceedings. (pp. 101-106). IEEE COMPUTER SOC. https://doi.org/10.1109/WDM.2001.990164

Vancouver

Pachet F, Westermann G, Laigre D. Musical data mining for electronic music distribution. In Nesi P, Bellini P, Busch C, editors, First International Conference on Web Delivering of Music, 2001. Proceedings. . LOS ALAMITOS: IEEE COMPUTER SOC. 2001. p. 101-106 doi: 10.1109/WDM.2001.990164

Author

Pachet, F ; Westermann, G ; Laigre, D . / Musical data mining for electronic music distribution. First International Conference on Web Delivering of Music, 2001. Proceedings. . editor / P Nesi ; P Bellini ; C Busch. LOS ALAMITOS : IEEE COMPUTER SOC, 2001. pp. 101-106

Bibtex

@inproceedings{0ad32432cea34816bb7d8fa3199a7c8e,

title = "Musical data mining for electronic music distribution",

abstract = "Music classification is a key ingredient for electronic music distribution. Because of the lack of standards in music classification - or the lack of enforcement of existing standards - there is a huge amount Of unclassified titles of music in the world. In this paper we propose a method of classification based on musical data mining technique based on co-occurrence and correlation analysis that can be used for classification. It gives a new approach of similarity between several titles of music or several artists. We study large corpora of textual information referring titles of music or artists whose names are decided by humans without particular constraints other than readability, and draw various hypotheses concerning the natural similarities that emerge from these corpora. Based on a clustering technique, we show that interesting groups can reveal specific music genres and allow classifying titles of music in a kind of objective manner.",

author = "F Pachet and G Westermann and D Laigre",

year = "2001",

doi = "10.1109/WDM.2001.990164",

language = "English",

isbn = "0-7695-1284-4",

pages = "101--106",

editor = "P Nesi and P Bellini and C Busch",

booktitle = "First International Conference on Web Delivering of Music, 2001. Proceedings.",

publisher = "IEEE COMPUTER SOC",

}

RIS

TY - GEN

T1 - Musical data mining for electronic music distribution

AU - Pachet, F

AU - Westermann, G

AU - Laigre, D

PY - 2001

Y1 - 2001

N2 - Music classification is a key ingredient for electronic music distribution. Because of the lack of standards in music classification - or the lack of enforcement of existing standards - there is a huge amount Of unclassified titles of music in the world. In this paper we propose a method of classification based on musical data mining technique based on co-occurrence and correlation analysis that can be used for classification. It gives a new approach of similarity between several titles of music or several artists. We study large corpora of textual information referring titles of music or artists whose names are decided by humans without particular constraints other than readability, and draw various hypotheses concerning the natural similarities that emerge from these corpora. Based on a clustering technique, we show that interesting groups can reveal specific music genres and allow classifying titles of music in a kind of objective manner.

AB - Music classification is a key ingredient for electronic music distribution. Because of the lack of standards in music classification - or the lack of enforcement of existing standards - there is a huge amount Of unclassified titles of music in the world. In this paper we propose a method of classification based on musical data mining technique based on co-occurrence and correlation analysis that can be used for classification. It gives a new approach of similarity between several titles of music or several artists. We study large corpora of textual information referring titles of music or artists whose names are decided by humans without particular constraints other than readability, and draw various hypotheses concerning the natural similarities that emerge from these corpora. Based on a clustering technique, we show that interesting groups can reveal specific music genres and allow classifying titles of music in a kind of objective manner.

U2 - 10.1109/WDM.2001.990164

DO - 10.1109/WDM.2001.990164

M3 - Conference contribution/Paper

SN - 0-7695-1284-4

SP - 101

EP - 106

BT - First International Conference on Web Delivering of Music, 2001. Proceedings.

A2 - Nesi, P

A2 - Bellini, P

A2 - Busch, C

PB - IEEE COMPUTER SOC

CY - LOS ALAMITOS

ER -

Research

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