Affective music recommendation systembased on the mood of input video

Shoto Sasaki, Tatsunori Hirai, Hayato Ohya, Shigeo Morishima

研究成果: Conference contribution

4 引用 (Scopus)

抜粋

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.

元の言語English
ホスト出版物のタイトルMultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings
編集者Xiangjian He, Dacheng Tao, Muhammad Abul Hasan, Suhuai Luo, Changsheng Xu, Jie Yang
出版者Springer Verlag
ページ299-302
ページ数4
ISBN(電子版)9783319144412
DOI
出版物ステータスPublished - 2015 1 1
イベント21st International Conference on MultiMedia Modeling, MMM 2015 - Sydney, Australia
継続期間: 2015 1 52015 1 7

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
8936
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Other

Other21st International Conference on MultiMedia Modeling, MMM 2015
Australia
Sydney
期間15/1/515/1/7

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Sasaki, S., Hirai, T., Ohya, H., & Morishima, S. (2015). Affective music recommendation systembased on the mood of input video. : X. He, D. Tao, M. A. Hasan, S. Luo, C. Xu, & J. Yang (版), MultiMedia Modeling - 21st International Conference, MMM 2015, Proceedings (pp. 299-302). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 8936). Springer Verlag. https://doi.org/10.1007/978-3-319-14442-9_33