• The Journal of the Convergence on Culture Technology
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• v.9 no.4
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• pp.309-314
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• 2023

This study examines the development and limitations of current artificial intelligence (AI) music composition programs. AI music composition programs have progressed significantly owing to deep learning technology. However, they possess limitations pertaining to the creative aspects of music. In this study, we collect, compare, and analyze information on existing AI-based music composition programs and explore their technical orientation, musical concept, and drawbacks to delineate future directions for AI music composition programs. Furthermore, this study emphasizes the importance of developing AI music composition programs that create "personalized" music, aligning with the era of personalization. Ultimately, for AI-based composition programs, it is critical to extensively research how music, as an output, can touch the listeners and implement appropriate changes. By doing so, AI-based music composition programs are expected to form a new structure in and advance the music industry.

• Trans-
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• v.13
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• pp.35-58
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• 2022

This study predicts the paradigm shift that the development of artificial intelligence technology will bring to the production of music content, and suggests that works created through collaboration between artificial intelligence and humans can have artistic value as finished products. Anyone can easily produce music content using artificial intelligence composition programs, and it has become an opportunity to inspire artists with various attempts and creative ideas. Although artificial intelligence technology provides convenience in human life and benefits a lot in the efficient aspect of work, it is difficult to escape the perception of data-based pattern music in the art field so far. Pattern music with many quantitative elements is not recognized as a complete creation due to the absence of abstract symbolism or meaning pursued by art. However, it predicts that if qualitative elements such as emotions and creativity are given to artificial intelligence music through human collaboration, it can be recognized as a complete work of art. The development of artificial intelligence technology increases access to culture and art from the public, and it can be expected that anyone can enjoy it as well as aesthetic experiences. In addition, various contents can be produced by improving individual digital literacy, and it is an opportunity to share and communicate with others. As such, artificial intelligence technology serves as a medium connecting the public with culture and art, and is narrowing the gap between humans and technology through art activities. Along with this cultural phenomenon, we predict the possibility of research on the production of artificial intelligence music contents with artistic value and the development of various convergence and complex art contents using artificial intelligence technology in the future.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.261-266
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• 2018

This study aimed to provide an overview of artificial intelligence based music composition programs. The artificial intelligence-based composition program has shown remarkable growth as the development of deep neural network theory and the improvement of big data processing technology. Accordingly, artificial intelligence based composition programs for composing classical music and pop music have been proposed variously in academia and industry. But there are several limitations: devaluation in general populations, missing valuable materials, lack of relevant laws, technology-led industries exclusive to the arts, and so on. When effective measures are taken against these limitations, artificial intelligence based technology will play a significant role in fostering national competitiveness.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.986-989
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• 2019

인공지능에 대한 연구가 계속되고 있지만 음악, 미술, 문학 등 창의력과 예술성을 요구하는 부문에서는 인공지능을 적용하기 어렵다. 하지만 그중 음악 부문에 인공지능을 접목시켜 기존에 없던 곡을 작곡해보고자 한다. LSTM 기반의 모델을 ABC Notation 악보 데이터를 활용하여 학습시켜 사용자들이 음악적 지식 없이도 새로운 음악을 작곡할 수 있도록 딥러닝을 기반으로 한 BGM 음원 작곡 서비스를 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.25 no.6
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• pp.25-33
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• 2020

In this paper, the purpose is to verify the impact of performance expectations, effort expectations, social impact, individual innovation and perceived value on the intent of use and the behavior of use. Used Unified Theory of Acceptance and Use of Technology (UTAUT) to verify the applicability of this model in China, and established the research model by adding two new variables to UTAUT according to the situation of the Chinese market. To achieve this goal, 345 questionnaires were collected for experienced music creators using artificial intelligence nuggets in China by means of Internet research. The collected data were analyzed through frequency analysis, factor analysis, reliability analysis, and structural equation analysis through SPSS V. 22.0 and AMOS V 22.0. The verification of the hypotheses presented in the research model identified the decisive influence factors on the use of artificial intelligence music acceptance by Chinese users. The study is innovative in that it attempts to verify the applicability of UTAUT in the Chinese context. In the construction of the user acceptance model of AI music, three influencing factors will have an effect on users' intentions, and according to the degree of effect, from largest to smallest, they are respectively Perceived Innovativeness, Performance Expectancy and Effort Expectancy. This paper will also provide some management advices, i.e. improving the utility and usability of AI music, encouraging users with individual innovativeness, developing competitive and attractive pricing policies, increasing publicity, and prioritizing word-of-mouth advertising.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.7
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• pp.869-876
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• 2021

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.

• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.41-50
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• 2021

While artificial intelligence is rapidly replacing human jobs, the art field where human creativity is important is considered an exception. There are currently several AI mastering services in the field of mastering music, a profession at the border between art and technology. In general, the quality of AI mastering is considered to be inferior to the work of a human professional mastering engineer. In this paper, acoustic analysis, listening experiments, and expert interviews were conducted to compare AI and human experts. In the acoustic analysis, In the analysis of audio, there was no significant difference between the results of professional mastering engineers and the results of artificial intelligence. In the listening experiment, the non-musicians could not distinguish between the sound quality of the professional mastering engineer's work and the artificial intelligence work. The group of musicians showed a preference for a specific sound source, but the preference for a specific mastering did not appear significantly. In an expert interview, In expert interviews, respondents answered that there was no significant difference in quality between the two mastering services, and the biggest difference was the communication method between the mastering service provider and the user. In addition, as data increases, it is expected that artificial intelligence mastering will achieve rapid quality improvement and further improvement in communication.

• Trans-
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• v.13
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• pp.117-140
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• 2022

Recently, artificial intelligence technology is being used throughout the industry. Currently, Currently, AI art generators are used in the NFT industry, and works using them have been exhibited and sold. AI art generators in the art field include Gated Photos, Google Deep Dream, Sketch-RNN, and Auto Draw. AI art generators in the music field are Beat Blender, Google Doodle Bach, AIVA, Duet, and Neural Synth. The characteristics of AI art generators are as follows. First, AI art generator in the art field are being used to create new works based on existing work data. Second, it is possible to quickly and quickly derive creative results to provide ideas to creators, or to implement various creative materials. In the future, AI art generators are expected to have a great influence on content planning and production such as visual art, music composition, literature, and movie.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.79-89
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• 2020

The current study explored if users' self-relevance of the comparison domain with creative AI robot in music composition affected their envy toward the robot, cognitive assessment toward the music and intention toward working with robot in future. This study conducted a 2 (degree of self-relevance: high(college students majoring in music) vs. low(those not majoring in music) × 2 (working type: robot-only vs. robot-human collaboration) between-subjects factorial design experiment. The findings revealed that those majoring in music did not feel envious of the robot as much as those not majoring in music. However, compared to those not majoring in music, those majoring in music evaluated the robot's creativity lower, had more negative attitude toward the music, showed less intention to use the music and work with the robots in future. No interaction between the degree of self-relevance and the working type was found.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.655-658
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• 2020

최근 IT 기술의 발달로 태블릿, 스마트폰과 같은 다양한 디바이스로 손쉽게 음악을 감상할 수 있다. 하지만 최근 이런 기술 발달과는 다르게 사용자가 원하는 음악을 검색하는 방법은 고전적인 형태에서 벗어나지 않고 있다. 기존 음악 검색 방법은 텍스트 기반, 내용 기반, 소비자 감성 기반의 음악 추천 검색 방법이 있으며 저장된 메타 데이터를 이용하여 사용자의 질의에 대한 결과만 제공할 뿐 사용자의 경험 정보를 고려하지 않는다. 그리고 기존 플랫폼들은 사용자가 최근 많이 들은 가수, 장르, 분위기를 종합하여 사용자에게 어울리는 음악을 추천을 할 뿐 사용자의 경험정보를 고려하여 음악을 추천하지는 않는다. 본 논문에서는 사용자의 경험 정보를 활용하여 사용자 맞춤형 음악 추천 시스템을 제안한다. 본 시스템은 사용자의 현재 기분 정보, 주변 날씨 정보 등을 입력 받는다. 이후, 경험 정보를 기반으로 결정 트리를 통해 사용자 요구 기반의 음악 추천 시스템을 구축하였다.