Genre classification for million song dataset using confidence-based classifiers combination

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

5 Scopus citations

Abstract

We proposed a method to classify songs in the Million Song Dataset according to song genre. Since songs have several data types, we trained sub-classifiers by different types of data. These sub-classifiers are combined using both classifier authority and classification confidence for a particular instance. In the experiments, the combined classifier surpasses all of these sub-classifiers and the SVM classifier using concatenated vectors from all data types. Finally, the genre labels for the Million Song Dataset are provided.

Original languageEnglish (US)
Title of host publicationSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval
Pages1083-1084
Number of pages2
DOIs
StatePublished - Sep 28 2012
Event35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012 - Portland, OR, United States
Duration: Aug 12 2012Aug 16 2012

Publication series

NameSIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval

Other

Other35th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012
CountryUnited States
CityPortland, OR
Period8/12/128/16/12

Keywords

  • classifier combination
  • song genre classification

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Hu, Y., & Ogihara, M. (2012). Genre classification for million song dataset using confidence-based classifiers combination. In SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1083-1084). (SIGIR'12 - Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval). https://doi.org/10.1145/2348283.2348480