Deep learning for Turkish makam music composition


Parlak İ. H., Çebi Y., Işıkhan C., Birant D.

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, vol.29, no.7, pp.3107-3118, 2021 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 7
  • Publication Date: 2021
  • Doi Number: 10.3906/elk-2101-44
  • Journal Name: TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, TR DİZİN (ULAKBİM)
  • Page Numbers: pp.3107-3118
  • Keywords: Turkish makam music, automatic composition, deep learning, machine learning, ART
  • Dokuz Eylül University Affiliated: Yes

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 Sarki 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.