Music genre classification using similarity functions

Yoko Anan, kohei hatano, Hideo Bannai, Masayuki Takeda

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

7 Citations (Scopus)

Abstract

We consider music classification problems. A typical machine learning approach is to use support vector machines with some kernels. This approach, however, does not seem to be successful enough for classifying music data in our experiments. In this paper, we follow an alternative approach. We employ a (dis)similarity-based learning framework proposed byWang et al. This (dis)similarity-based approach has a theoretical guarantee that one can obtain accurate classifiers using (dis)similarity measures under a natural assumption. We demonstrate the effectiveness of our approach in computational experiments using Japanese MIDI data.

Original languageEnglish
Title of host publicationProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011
Pages693-698
Number of pages6
Publication statusPublished - Dec 1 2011
Event12th International Society for Music Information Retrieval Conference, ISMIR 2011 - Miami, FL, United States
Duration: Oct 24 2011Oct 28 2011

Publication series

NameProceedings of the 12th International Society for Music Information Retrieval Conference, ISMIR 2011

Other

Other12th International Society for Music Information Retrieval Conference, ISMIR 2011
CountryUnited States
CityMiami, FL
Period10/24/1110/28/11

All Science Journal Classification (ASJC) codes

  • Music
  • Information Systems

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