Deep learning for Turkish makam music composition

Authors

İSMAİL HAKKI PARLAK
YALÇIN ÇEBİ
CİHAN IŞIKHAN
DERYA BİRANT

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.

DOI

10.3906/elk-2101-44

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