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
Country/TerritoryUnited States
CityPortland, OR
Period8/12/128/16/12

Keywords

  • classifier combination
  • song genre classification

ASJC Scopus subject areas

  • Information Systems

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