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http://dx.doi.org/10.6109/jkiice.2021.25.7.869

Korean Traditional Music Melody Generator using Artificial Intelligence  

Bae, Jun (Department of Music Technology, The University of Suwon)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.7, 2021 , pp. 869-876 More about this Journal
Abstract
In the field of music, various AI composition methods using machine learning have recently been attempted. However, most of this research has been centered on Western music, and little research has been done on Korean traditional music. Therefore, in this paper, we will create a data set of Korean traditional music, create a melody using three algorithms based on the data set, and compare the results. Three models were selected based on the similarity between language and music, LSTM, Music Transformer and Self Attention. Using each of the three models, a melody generator was modeled and trained to generate melodies. As a result of user evaluation, the Self Attention method showed higher preference than the other methods. Data set is very important in AI composition. For this, a Korean traditional music data set was created, and AI composition was attempted with various algorithms, and this is expected to be helpful in future research on AI composition for Korean traditional music.
Keywords
AI; Deep learning; Korean traditional music; Algorithm composition;
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