IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i3id195.html

Music Genre Classification using Machine Learning on FMA Dataset

Author

Listed:
  • Sathvik Reddy B P
  • Nikitha R
  • Shilpa T U
  • Shankar N. B

Abstract

Music is a universal form of expression with a multitude of genres that resonate with diverse audiences. While genre classification may seem straightforward to the human ear, automating this process poses a complex challenge. This complexity stems from the subtle and intricate characteristics that differentiate one musical genre from another. Effective categorization not only has implications for how music is organized and recommended in digital platforms but can also provide insights into the underlying structure and semantics of musical compositions. To tackle this issue, we aim to utilize machine learning and deep learning techniques to automatically categorize music into genres.

Suggested Citation

  • Sathvik Reddy B P & Nikitha R & Shilpa T U & Shankar N. B, 2024. "Music Genre Classification using Machine Learning on FMA Dataset," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(3), pages 226-231, June.
  • Handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:195
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT2410325
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT2410325
    File Function: Article URL
    Download Restriction: no

    File URL: https://ijsrcseit.com/home/article/download/CSEIT2410325/CSEIT2410325
    File Function: Full text
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:jbh:ijsrcs:v10:y2024:i3:id:195. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pankaj Sharma (USA) (email available below). General contact details of provider: https://ijsrcseit.com/home .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.