Abstract
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Machine learning research on image recognition and processing has been actively conducted over the past decade. In the field of music, various methods using machine learning have been recently attempted, showing the possibility of creating melody and harmony that humans cannot make. However, most of...

Machine learning research on image recognition and processing has been actively conducted over the past decade. In the field of music, various methods using machine learning have been recently attempted, showing the possibility of creating melody and harmony that humans cannot make. However, most of the research on artificial intelligence composition has been centered on Western music, and little research has been done on Korean traditional music. In particular, there were many difficulties in research because even a data set for research was not created.

Therefore, in this paper, we will create a data set of Korean traditional music, create a Korean traditional music melody using three algorithms based on the data set, and compare the results. The purpose of this thesis is to better understand the structure and characteristics of Korean traditional music through big data analysis of Korean traditional music, and to create a Korean traditional music melody generator model using artificial intelligence based on it.

The criterion for the selection of algorithms and architectures was how well the algorithms exhibited excellent performance in deriving results suitable for creating Korean traditional music melodies. As a result of reviewing the existing research algorithms, it was found that a deep learning algorithm based on text is suitable for generating musical melodies. As a result, LSTM, Music Transformer and Self Attention 3 models based on the similarity between language and music were selected.

It is necessary to pre-process the data in order to learn Korean traditional music data by inputting it into a neural network. To this end, Korean songs were converted into MIDI, and they were converted into vectors using the Music21 library.

Using each of the three models, a Korean traditional music melody generator was modeled and trained to generate a Korean traditional music melody. When comparing the three methods, the self-attention model was the model that produced the melody that was closest to the feeling of Korean traditional music. The LSTM model produced a melody that resembles Arirang, one of the training data, too repetitively, and it was not able to properly express the pros and cons of Korean traditional music. The music transformer model used 10 tone scales, which used a lot of semitones, unlike traditional traditional music, which mainly used 5 tone scales, and the resulting melody was reminiscent of the piano sonata of Western classical music.

Since the evaluation of music is not objective but depends on individual preferences, it is necessary to perform user evaluation rather than accuracy evaluation, so a user survey was conducted. The results of the melodies of Korean traditional music of the three models were converted into sound sources and played to people, and the score was evaluated on a 5-point scale to determine which songs best used the feeling of Korean music. As a result of user evaluation, a difference of 0.76 points compared to the Self Attention method from LSTM method and 0.67 points from the Music transformer method showed high preference.

Data representation and training data are very important in artificial intelligence composition. For this, a basic Korean music data set was created, and artificial intelligence composition was attempted with various algorithms, and this is expected to be helpful in future research on artificial intelligence composition for Korean traditional music.

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