Deep learning for Turkish makam music composition

Authors

İSMAİL HAKKI PARLAK
YALÇIN ÇEBİ
CİHAN IŞIKHAN
DERYA BİRANT

DOI

10.3906/elk-2101-44

Abstract

In this paper, we introduce a new deep-learning-based system that can compose structured Turkish makam music (TMM) in the symbolic domain. Presented artificial TMM composer (ATMMC) takes eight initial notes from a human user and completes the rest of the piece. The backbone of the composer system consists of multilayered long short-term memory (LSTM) networks. ATMMC can create pieces in Hicaz and Nihavent makams in Şarkı form, which can be viewed and played with Mus2, a notation software for microtonal music. Statistical analysis shows that pieces composed by ATMMC are approximately 84% similar to training data. ATMMC is an open-source project and can assist Turkish makam music enthusiasts with creating new pieces for professional, educational, or entertainment purposes.

Keywords

Turkish makam music, automatic composition, deep learning, machine learning

First Page

3107

Last Page

3118

Recommended Citation

PARLAK, İSMAİL HAKKI; ÇEBİ, YALÇIN; IŞIKHAN, CİHAN; and BİRANT, DERYA (2021) "Deep learning for Turkish makam music composition," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29: No. 7, Article 13. https://doi.org/10.3906/elk-2101-44
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss7/13