Affective music recommendation system using input images

Shoto Sasaki, Tatsunori Hirai, Hayato Ohya, Shigeo Morishima

    研究成果: Conference contribution

    4 引用 (Scopus)

    抄録

    Music that matches our current mood can create a deep impression, which we usually want to enjoy when we listen to music. However, we do not know which music best matches our present mood. We have to listen to each song, searching for music that matches our mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. Most recommendation methods, such as collaborative filtering or content similarity, do not target a specific mood. In addition, there may be no word exactly specifying the mood. Therefore, textual retrieval is not effective. 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. We now present an affective music recommendation system using an input image without textual information.

    元の言語English
    ホスト出版物のタイトルACM SIGGRAPH 2013 Posters, SIGGRAPH 2013
    DOI
    出版物ステータスPublished - 2013
    イベントACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013 - Anaheim, CA
    継続期間: 2013 7 212013 7 25

    Other

    OtherACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013
    Anaheim, CA
    期間13/7/2113/7/25

    Fingerprint

    Recommender systems
    Collaborative filtering

    ASJC Scopus subject areas

    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Software

    これを引用

    Sasaki, S., Hirai, T., Ohya, H., & Morishima, S. (2013). Affective music recommendation system using input images. : ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013 [90] https://doi.org/10.1145/2503385.2503484

    Affective music recommendation system using input images. / Sasaki, Shoto; Hirai, Tatsunori; Ohya, Hayato; Morishima, Shigeo.

    ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013. 2013. 90.

    研究成果: Conference contribution

    Sasaki, S, Hirai, T, Ohya, H & Morishima, S 2013, Affective music recommendation system using input images. : ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013., 90, ACM Special Interest Group on Computer Graphics and Interactive Techniques Conference, SIGGRAPH 2013, Anaheim, CA, 13/7/21. https://doi.org/10.1145/2503385.2503484
    Sasaki S, Hirai T, Ohya H, Morishima S. Affective music recommendation system using input images. : ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013. 2013. 90 https://doi.org/10.1145/2503385.2503484
    Sasaki, Shoto ; Hirai, Tatsunori ; Ohya, Hayato ; Morishima, Shigeo. / Affective music recommendation system using input images. ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013. 2013.
    @inproceedings{229fe2b14bb64cd6be09ab31f3a4afb3,
    title = "Affective music recommendation system using input images",
    abstract = "Music that matches our current mood can create a deep impression, which we usually want to enjoy when we listen to music. However, we do not know which music best matches our present mood. We have to listen to each song, searching for music that matches our mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. Most recommendation methods, such as collaborative filtering or content similarity, do not target a specific mood. In addition, there may be no word exactly specifying the mood. Therefore, textual retrieval is not effective. 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. We now present an affective music recommendation system using an input image without textual information.",
    author = "Shoto Sasaki and Tatsunori Hirai and Hayato Ohya and Shigeo Morishima",
    year = "2013",
    doi = "10.1145/2503385.2503484",
    language = "English",
    isbn = "9781450323420",
    booktitle = "ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013",

    }

    TY - GEN

    T1 - Affective music recommendation system using input images

    AU - Sasaki, Shoto

    AU - Hirai, Tatsunori

    AU - Ohya, Hayato

    AU - Morishima, Shigeo

    PY - 2013

    Y1 - 2013

    N2 - Music that matches our current mood can create a deep impression, which we usually want to enjoy when we listen to music. However, we do not know which music best matches our present mood. We have to listen to each song, searching for music that matches our mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. Most recommendation methods, such as collaborative filtering or content similarity, do not target a specific mood. In addition, there may be no word exactly specifying the mood. Therefore, textual retrieval is not effective. 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. We now present an affective music recommendation system using an input image without textual information.

    AB - Music that matches our current mood can create a deep impression, which we usually want to enjoy when we listen to music. However, we do not know which music best matches our present mood. We have to listen to each song, searching for music that matches our mood. As it is difficult to select music manually, we need a recommendation system that can operate affectively. Most recommendation methods, such as collaborative filtering or content similarity, do not target a specific mood. In addition, there may be no word exactly specifying the mood. Therefore, textual retrieval is not effective. 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. We now present an affective music recommendation system using an input image without textual information.

    UR - http://www.scopus.com/inward/record.url?scp=84881581215&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=84881581215&partnerID=8YFLogxK

    U2 - 10.1145/2503385.2503484

    DO - 10.1145/2503385.2503484

    M3 - Conference contribution

    SN - 9781450323420

    BT - ACM SIGGRAPH 2013 Posters, SIGGRAPH 2013

    ER -