Music artist style identification by semi-supervised learning from both lyrics and content

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

21 Scopus citations

Abstract

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. The approach for using a small set of labeled samples for the seed labeling to build classifiers that improve themselves using unlabeled data is presented. This approach is tested on a data set consisting of 43 artists and 56 albums using artist similarity provided by All Music Guide. Experimental results show that using such an approach the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.

Original languageEnglish (US)
Title of host publicationACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
PublisherAssociation for Computing Machinery
Pages364-367
Number of pages4
ISBN (Print)1581138938, 9781581138931
DOIs
StatePublished - 2004
EventACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia - New York, NY, United States
Duration: Oct 10 2004Oct 16 2004

Publication series

NameACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia

Other

OtherACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia
CountryUnited States
CityNew York, NY
Period10/10/0410/16/04

Keywords

  • Artist style
  • Lyrics
  • Semi-supervised learning

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Li, T., & Ogihara, M. (2004). Music artist style identification by semi-supervised learning from both lyrics and content. In ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia (pp. 364-367). (ACM Multimedia 2004 - proceedings of the 12th ACM International Conference on Multimedia). Association for Computing Machinery. https://doi.org/10.1145/1027527.1027612