Affective music recommendation systembased on the mood of input video

Shoto Sasaki, Tatsunori Hirai, Hayato Ohya, Shigeo Morishima

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

11 Citations (Scopus)


We present an affective music recommendation system just fitting to an input video without textual information. Music that matches our current environmental mood can enhance a deep impression. However, we cannot know easily which music best matches our present mood from huge music database. So we often select a well-known popular song repeatedly in spite of the present mood. In this paper, we analyze the video sequence which represent current mood and recommend an appropriate music which affects the current mood. Our system matches an input video with music using valence-arousal plane which is an emotional plane.

Original languageEnglish
Title of host publicationMultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
EditorsXiangjian He, Dacheng Tao, Muhammad Abul Hasan, Suhuai Luo, Changsheng Xu, Jie Yang
PublisherSpringer Verlag
Number of pages4
ISBN (Electronic)9783319144412
Publication statusPublished - 2015
Event21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney, Australia
Duration: 2015 Jan 52015 Jan 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st International Conference on MultiMedia Modeling, MMM 2015


  • Arousal
  • Image processing
  • Music recommendation
  • Valence

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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