Automatic Melody Composition Using Enhanced GAN

Shuyu Li, Sejun Jang and Yunsick Sung
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Shuyu Li: Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea
Sejun Jang: Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea
Yunsick Sung: Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea

Mathematics, 2019, vol. 7, issue 10, 1-13

Abstract: In traditional music composition, the composer has a special knowledge of music and combines emotion and creative experience to create music. As computer technology has evolved, various music-related technologies have been developed. To create new music, a considerable amount of time is required. Therefore, a system is required that can automatically compose music from input music. This study proposes a novel melody composition method that enhanced the original generative adversarial network (GAN) model based on individual bars. Two discriminators were used to form the enhanced GAN model: one was a long short-term memory (LSTM) model that was used to ensure correlation between the bars, and the other was a convolutional neural network (CNN) model that was used to ensure rationality of the bar structure. Experiments were conducted using bar encoding and the enhanced GAN model to compose a new melody and evaluate the quality of the composition melody. In the evaluation method, the TFIDF algorithm was also used to calculate the structural differences between four types of musical instrument digital interface (MIDI) file (i.e., randomly composed melody, melody composed by the original GAN, melody composed by the proposed method, and the real melody). Using the TFIDF algorithm, the structures of the melody composed were compared by the proposed method with the real melody and the structure of the traditional melody was compared with the structure of the real melody. The experimental results showed that the melody composed by the proposed method had more similarity with real melody structure with a difference of only 8% than that of the traditional melody structure.

Keywords: convolutional neural network; deep learning; generative adversarial network; long short-term memory; melody composition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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