Turkish Makam Music Composition by Using Deep Learning Techniques

Although music and other forms of fine arts have been accepted as a part of human existence, people have developed various methods and algorithms throughout history to create new creations in these domains. Especially in the last century, with the invention of the computer and the exponential increa...

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Bibliographic Details
Main Author:	Parlak, İsmail Hakki
Format:	Dissertation
Language:	English
Published:	ProQuest Dissertations & Theses 01-01-2021
Subjects:	Artificial intelligence Middle Eastern Studies Music Musical composition Musicians & conductors Neural networks User interface
Online Access:	Get full text
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Summary:	Although music and other forms of fine arts have been accepted as a part of human existence, people have developed various methods and algorithms throughout history to create new creations in these domains. Especially in the last century, with the invention of the computer and the exponential increase in its processing capacity, new computational methods in artistic creativity have been tried and interesting results have been obtained in related fields. Artificial composers, developed with Deep Learning techniques, which is a sub-field of artificial intelligence, took part in interdisciplinary studies and their artificial compositions began to be followed by those who are interested in the subject with great curiosity and interest. However, the studies of composing music using Deep Learning techniques were mostly performed on Western Music, and Turkish Maqam Music remained untouched in this arena.In the execution of this thesis, a system that can automatically compose Turkish Makam Music using Deep Learning techniques and an easy-to-use web-browser based graphical interface has been developed. The system, called Automatic Turkish Makam Music Composer (ATMMC), takes 8 starting notes from its user and creates a composition in Aksak or Düyek Usûls in one of Hicaz or Nihâvent Makams, depending on the user's preference. Artificial compositions created by ATMMC can be stored in the user’s computer to be opened with Mus2 application. Generated artificial compositions were compared with the source data set according to various metrics and it was seen that there was approximately 84% similarity between the source dataset and artificial compositions. The developed system and its user interface are shared as an open-source project.
ISBN:	9798381572452