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Moodsically. Personal Music Management Tool with Automatic Classification of Emotions

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Distributed Computing and Artificial Intelligence, 15th International Conference (DCAI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 800))

Abstract

It is a fact that music is directly linked to emotions. Various researches study the link between musical characteristics and the feeling produced or even induced. This work shows a web tool that allows the automatically extraction of musical characteristics of songs including the emotional classification and it uses this metadata to manage the user playlist in streaming. The objective of this work has been to contribute to improve a streaming music tool with perceptual characteristics associated with emotions and musical descriptors elements. The tool provides profile management, such as search engine, customizable playlist generation and song’s recommender alignment with emotional elements associated with music characteristics.

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Notes

  1. 1.

    https://open.spotify.com/.

  2. 2.

    http://www.radioline.co/online-radios-podcasts/moods.

  3. 3.

    http://rockola.fm/.

  4. 4.

    https://www.allmusic.com/advanced-search.

  5. 5.

    http://essentia.upf.edu/.

  6. 6.

    http://essentia.upf.edu/documentation/gaia/.

  7. 7.

    https://en.wikipedia.org/wiki/Representational_state_transfer.

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Acknowledgments

This work was supported by the Spanish Ministry of Economy and Competitiveness and FEDER funds. Project SURF (TIN2015-65515-C4-3-R).

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Authors and Affiliations

  1. Dpto. de Informática y Automática – Facultad de Ciencias, University of Salamanca, Plaza de la Merced s/n, 37008, Salamanca, Spain

    Jorge García Vicente, Ana B. Gil, Ana de Luis Reboredo, Diego Sánchez-Moreno & María N. Moreno-García

Authors
  1. Jorge García Vicente
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  2. Ana B. Gil
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  3. Ana de Luis Reboredo
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  4. Diego Sánchez-Moreno
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  5. María N. Moreno-García
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Corresponding author

Correspondence to Ana B. Gil .

Editor information

Editors and Affiliations

  1. BISITE Digital Innovation Hub. Edificio Multimedia I+D+i, University of Salamanca, Salamanca, Spain

    Fernando De La Prieta

  2. Faculty of Engineering, Department of Electronics, Information and Communication, Osaka Institute of Technology, Osaka, Osaka, Japan

    Sigeru Omatu

  3. Department of Computing Systems, School of Industrial Engineers at Albacete, University of Castilla-La Mancha, Albacete, Spain

    Antonio Fernández-Caballero

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Vicente, J.G., Gil, A.B., de Luis Reboredo, A., Sánchez-Moreno, D., Moreno-García, M.N. (2019). Moodsically. Personal Music Management Tool with Automatic Classification of Emotions. In: De La Prieta, F., Omatu, S., Fernández-Caballero, A. (eds) Distributed Computing and Artificial Intelligence, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 800. Springer, Cham. https://doi.org/10.1007/978-3-319-94649-8_14

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