Turkish Journal of Electrical Engineering and Computer Sciences
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.
Turkish makam music, automatic composition, deep learning, machine learning
PARLAK, İSMAİL HAKKI; ÇEBİ, YALÇIN; IŞIKHAN, CİHAN; and BİRANT, DERYA
"Deep learning for Turkish makam music composition,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 29:
7, Article 13.
Available at: https://journals.tubitak.gov.tr/elektrik/vol29/iss7/13
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