Music Genre Classification and Recommendation by Using Machine Learning Techniques

Ahmet Elbir, Hilmi Bilal Çam, Mehmet Emre Iyican, Berkay Öztürk, Nizamettin Aydin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

45 Citations (Scopus)

Abstract

Music genre prediction is one of the topics that digital music processing is interested in. In this study, acoustic features of music have been extracted by using digital signal processing techniques and then music genre classification and music recommendations have been made by using machine learning methods. In addition, convolutional neural networks, which are deep learning methods, were used for genre classification and music recommendation and performance comparison of the obtained results has been. In the study, GTZAN database has been used and the highest success was obtained with the SVM algorithm.

Original languageEnglish
Title of host publicationProceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018
EditorsTulay Yildirim, Buse Melis Ozyildirim
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538677865
DOIs
Publication statusPublished - 29 Nov 2018
Externally publishedYes
Event2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 - Adana, Turkey
Duration: 4 Oct 20186 Oct 2018

Publication series

NameProceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018

Conference

Conference2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018
Country/TerritoryTurkey
CityAdana
Period4/10/186/10/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Acoustic features
  • Deep Learning
  • Machine Learning
  • Music genre Classification

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