Affective music recommendation system reflecting the mood of input image

Shoto Sasaki, Tatsunori Hirai, Hayato Ohya, Shigeo Morishima

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

5 Citations (Scopus)

Abstract

We present an affective music recommendation system using input images without textual information. Music that matches our current mood can create a deep impression. However, we do not know which music best matches our present mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. In this paper, we assume that there exists a relationship between our mood and images because visual information affects our mood when we listen to music. Our system matches an input image with music using valence-arousal plane which is an emotional plane.

Original languageEnglish
Title of host publicationProceedings - 2013 International Conference on Culture and Computing, Culture and Computing 2013
PublisherIEEE Computer Society
Pages153-154
Number of pages2
ISBN (Print)9780769550473
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 International Conference on Culture and Computing, Culture and Computing 2013 - Kyoto, Japan
Duration: 2013 Sep 162013 Sep 18

Publication series

NameProceedings - 2013 International Conference on Culture and Computing, Culture and Computing 2013

Conference

Conference2013 International Conference on Culture and Computing, Culture and Computing 2013
CountryJapan
CityKyoto
Period13/9/1613/9/18

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Keywords

  • Arousal
  • Image processing
  • Images and sounds
  • Music recommendation
  • Valence

ASJC Scopus subject areas

  • Software

Cite this

Sasaki, S., Hirai, T., Ohya, H., & Morishima, S. (2013). Affective music recommendation system reflecting the mood of input image. In Proceedings - 2013 International Conference on Culture and Computing, Culture and Computing 2013 (pp. 153-154). [6680355] (Proceedings - 2013 International Conference on Culture and Computing, Culture and Computing 2013). IEEE Computer Society. https://doi.org/10.1109/CultureComputing.2013.42