Efficient feature mining in music objects

Jia-Ling Koh, William D.C. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

This paper proposes novel strategies for efficiently extracting repeating patterns and frequent note sequences in music objects. Based on bit stream representation, the bit index sequences are designed for representing the whole note sequence of a music object with little space requirement. Besides, the proposed algorithm counts the repeating frequency of a pattern efficiently to rapidly extracting repeating patterns in a music object. Moreover, with the assist of appearing bit sequences, another algorithm is proposed for verifying the frequent note sequences in a set of music objects efficiently. Experimental results demonstrate that the performance of the proposed approach is more efficient than the related works.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings
EditorsPavel Vogel, Gerald Quirchmayr, Heinrich C. Mayr, Jiri Lazansky
PublisherSpringer Verlag
Pages221-231
Number of pages11
ISBN (Print)3540425276, 9783540425274
Publication statusPublished - 2001 Jan 1
Event12th International Conference on Database and Expert Systems Applications, DEXA 2001 - Munich, Germany
Duration: 2001 Sep 32001 Sep 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2113
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other12th International Conference on Database and Expert Systems Applications, DEXA 2001
CountryGermany
CityMunich
Period01/9/301/9/5

Fingerprint

Music
Mining
Count
Object
Requirements
Experimental Results
Demonstrate

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Koh, J-L., & Yu, W. D. C. (2001). Efficient feature mining in music objects. In P. Vogel, G. Quirchmayr, H. C. Mayr, & J. Lazansky (Eds.), Database and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings (pp. 221-231). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2113). Springer Verlag.

Efficient feature mining in music objects. / Koh, Jia-Ling; Yu, William D.C.

Database and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings. ed. / Pavel Vogel; Gerald Quirchmayr; Heinrich C. Mayr; Jiri Lazansky. Springer Verlag, 2001. p. 221-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2113).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Koh, J-L & Yu, WDC 2001, Efficient feature mining in music objects. in P Vogel, G Quirchmayr, HC Mayr & J Lazansky (eds), Database and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2113, Springer Verlag, pp. 221-231, 12th International Conference on Database and Expert Systems Applications, DEXA 2001, Munich, Germany, 01/9/3.
Koh J-L, Yu WDC. Efficient feature mining in music objects. In Vogel P, Quirchmayr G, Mayr HC, Lazansky J, editors, Database and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings. Springer Verlag. 2001. p. 221-231. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Koh, Jia-Ling ; Yu, William D.C. / Efficient feature mining in music objects. Database and Expert Systems Applications - 12th International Conference, DEXA 2001, Proceedings. editor / Pavel Vogel ; Gerald Quirchmayr ; Heinrich C. Mayr ; Jiri Lazansky. Springer Verlag, 2001. pp. 221-231 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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