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Music Industry Trend Forecasting Based on MusicBrainz Metadata

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13758))

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Abstract

In this paper forecast analysis for music industry is performed. The trends for years 2020–2024 are calculated based on forecasting for time-series metadata from online MusicBrainz dataset. The analysis takes on music releases throughout the years from different perspectives, e.g. the release format, type of music releases or release length (playback time). In the end, all the results are discussed before final conclusions are drawn.

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Notes

  1. 1.

    https://musicbrainz.org/.

  2. 2.

    https://www.discogs.com/.

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Author information

Authors and Affiliations

  1. Faculty of Information and Communication Technology, Wroclaw University of Science and Technology, Wybrzeze Wyspiańskiego 27, 50-370, Wroclaw, Poland

    Marek Kopel & Damian Kreisich

Authors
  1. Marek Kopel
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  2. Damian Kreisich
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Corresponding author

Correspondence to Marek Kopel .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Kopel, M., Kreisich, D. (2022). Music Industry Trend Forecasting Based on MusicBrainz Metadata. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13758. Springer, Cham. https://doi.org/10.1007/978-3-031-21967-2_47

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  • DOI: https://doi.org/10.1007/978-3-031-21967-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21966-5

  • Online ISBN: 978-3-031-21967-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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