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 language | English |
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Title of host publication | Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 |
Editors | Tulay Yildirim, Buse Melis Ozyildirim |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781538677865 |
DOIs | |
Publication status | Published - 29 Nov 2018 |
Externally published | Yes |
Event | 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 - Adana, Turkey Duration: 4 Oct 2018 → 6 Oct 2018 |
Publication series
Name | Proceedings - 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 |
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Conference
Conference | 2018 Innovations in Intelligent Systems and Applications Conference, ASYU 2018 |
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Country/Territory | Turkey |
City | Adana |
Period | 4/10/18 → 6/10/18 |
Bibliographical note
Publisher Copyright:© 2018 IEEE.
Keywords
- Acoustic features
- Deep Learning
- Machine Learning
- Music genre Classification