SVR-based music mood classification and context-based music recommendation

Seungmin Rho, Byeong Jun Han, Eenjun Hwang

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

43 Citations (Scopus)

Abstract

With the advent of the ubiquitous era, context-based music recommendation has become one of rapidly emerging applications. Context-based music recommendation requires multidisciplinary efforts including low level feature extraction, music mood classification and human emotion prediction. Especially, in this paper, we focus on the implementation issues of context-based mood classification and music recommendation. For mood classification, we reformulate it into a regression problem based on support vector regression (SVR). Through the use of the SVR-based mood classifier, we achieved 87.8% accuracy. For music recommendation, we reason about the user's mood and situation using both collaborative filtering and ontology technology. We implement a prototype music recommendation system based on this scheme and report some of the results that we obtained.

Original languageEnglish
Title of host publicationMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums
Pages713-716
Number of pages4
DOIs
Publication statusPublished - 2009
Event17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums - Beijing, China
Duration: 2009 Oct 192009 Oct 24

Publication series

NameMM'09 - Proceedings of the 2009 ACM Multimedia Conference, with Co-located Workshops and Symposiums

Other

Other17th ACM International Conference on Multimedia, MM'09, with Co-located Workshops and Symposiums
Country/TerritoryChina
CityBeijing
Period09/10/1909/10/24

Keywords

  • Classification
  • Music mood
  • Ontology
  • Recommendation
  • Support vector regression

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Software

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