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100+ datasets found
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  2. Data from: Music features

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  3. Favorite music genres in the U.S. 2018, by age

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  4. Data from: MusicOSet: An Enhanced Open Dataset for Music Data Mining

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  5. d

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  6. Music listening frequency in the U.S. 2019, by age group

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    The Italian Music Dataset

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    • zenodo.org
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  8. MGD: Music Genre Dataset

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  9. P

    RWC Dataset

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  10. Music Data Sharing Platform for Academic Research (CCMusic)

    • zenodo.org
    zip
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  11. P

    Emomusic Dataset

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  12. Streamed music consumption in the U.S. 2021, by genre

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  13. Main Dataset for "Evolution of Popular Music: USA 1960–2010"

    • figshare.com
    • search.datacite.org
    txt
    Updated Jan 19, 2016
  14. m

    Music Dataset: Lyrics and Metadata from 1950 to 2019

    • data.mendeley.com
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    Updated Oct 23, 2020
  15. m

    Music Dataset: Lyrics and Metadata from 1950 to 2019

    • data.mendeley.com
    • www.narcis.nl
    Updated Aug 24, 2020
  16. Global revenue of the recorded music industry 1999-2020

    • www.statista.com
    • www-statista-com.libdata2015.hilbert.edu
    • +1more
  17. Music consumption in the U.S. in 2020, by genre & format

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  18. Music album consumption U.S. 2018, by genre

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    Updated May 3, 2019
  19. Music sales value: digital and physical music from Spain 2001-2020

    • www.statista.com
    Updated Jul 2, 2021
  20. m

    Music Topics and Metadata

    • data.mendeley.com
    Updated Aug 22, 2020
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Zhaorui Liu; Zijin Li (2021). Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET) [Dataset]. http://doi.org/10.5281/zenodo.5676893
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Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)

zipAvailable download formats
Dataset updated
Nov 12, 2021
Dataset provided by
China Conservatory of Musichttp://www.ccmusic.edu.cn/
http://www.ccom.edu.cn/
Authors
Zhaorui Liu; Zijin Li
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Description

This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.

This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.

If you want to know more information or obtain complete files, please go to the official website of this platform:

Music Data Sharing Platform for Academic Research

  • Chinese Traditional Instrument Sound Database (CTIS)

This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.

  • Multi-functional Music Database for MIR Research

This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.

  • Midi-wav Bi-directional Database of Pop Music

This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.

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