• 통상정보연구
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• 제7권4호
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• pp.3-21
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• 2005

Music industry confronts turbulent change through the explosive usage of internet in downloading music files by consumers. Internet substitutes traditional music distributional channel and develops new digital music market. This study examines what change has been brought to music industry in both domestic market and global market by the usage of internet. The study also discusses some cases and legal issues in digital music industry.

• International Journal of Computer Science & Network Security
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• 제21권12호
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• pp.300-308
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• 2021

The processes of informatization of the modern educational space are inextricably linked with the active introduction of innovative information technologies, which diversify the forms of education and upbringing. The use of these technologies in education due to their specific properties significantly enhances the clarity of learning, emotional impact on students, helps to deepen interdisciplinary links, intensifies students' work, and improves the organization of educational activities. Innovative information technologies offer new opportunities for the use of text, audio, graphic, and video information in lessons, enriching the methodological possibilities of the lesson. Today, the use of these technologies is becoming an integral part of the study of any subject. Using multimedia presentations, publications, and websites created by students in the learning process, they can develop learning skills. According to researchers, there are many multimedia programs for working with a computer in a music lesson, namely: a music player, a program for singing karaoke, a music constructor, music encyclopedias, and training programs. The introduction of innovative information technologies in the system of music education allows expanding learning opportunities.

• 한국컴퓨터정보학회논문지
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• 제22권8호
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• pp.85-91
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• 2017

Although various music composition programs for PCs have become commonplace, the reasons why people think it is hard to make music are the lack of experience with musical instruments, the lack of knowledge of music or composition, and the fear of learning MIDI software. In this paper, we propose an effective method to solve this problem by using plain text based method which makes it easy for the general people who do not know MIDI, have little experience of musical instruments, and cannot even read music to make their own music. As a result, many people who like music but have not been able to produce their own music may produce and distribute music, and collaborate with others to produce better quality music.

• The Journal of the Acoustical Society of Korea
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• 제27권2E호
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• pp.51-56
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• 2008

In this paper, we propose an effective method for music summarization which automatically extracts a representative part of the music by using signal processing technology. Proposed method uses a vector quantization technique to extract several segments which can be regarded as the most important contents in the music. In general, there is a repetitive pattern in music, and human usually recognizes the most important or catchy tune from the repetitive pattern. Thus the repetition which is extracted using segment similarity is considered to express a music summary. The segments extracted are again combined to generate a complete music summary. Experiments show the proposed method captures the main theme of the music more effectively than conventional methods. The experimental results also show that the proposed method could be used for real-time application since the processing time in generating music summary is much faster than other methods.

• The Journal of the Acoustical Society of Korea
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• 제26권2E호
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• pp.39-43
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• 2007

Music summarization refers to a technique which automatically extracts the most important and representative segments in music content. In this paper, we propose and evaluate a technique which provides the repeated part in music content as music summary. For extracting a repeated segment in music content, the proposed algorithm uses the weighted sum of similarity measures based on multi-level vector quantization for fixed-length summary or optimal-length summary. For similarity measures, count-based similarity measure and distance-based similarity measure are proposed. The number of the same codeword and the Mahalanobis distance of features which have same codeword at the same position in segments are used for count-based and distance-based similarity measure, respectively. Fixed-length music summary is evaluated by measuring the overlapping ratio between hand-made repeated parts and automatically generated ones. Optimal-length music summary is evaluated by calculating how much automatically generated music summary includes repeated parts of the music content. From experiments we observed that optimal-length summary could capture the repeated parts in music content more effectively in terms of summary length than fixed-length summary.

• 한국정보관리학회:학술대회논문집
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• 한국정보관리학회 2017년도 제24회 학술대회 논문집
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• pp.23-32
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• 2017

Music therapy has been proved to be effective in treatment of diseases such as Alzheimer's disease. Many studies have investigated the effect of music therapy techniques on symptoms of a given disease but there has been no efforts in classifying those studies by specific symptoms of diseases, although patients, caregivers and music therapists have difficulty in discovering documents that they need to treat certain diseases. Thus, in the study, we propose a method to group music therapy-related publications by the music therapy techniques mainly used for a given disease. We expect that it will help music therapists and patients to find papers to help them to cure a specific disorder.

• 한국컴퓨터정보학회논문지
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• 제20권10호
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• pp.69-76
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• 2015

User's selection of music is largely influenced by private tastes as well as emotional states, and it is the unconsciousness projection of user's emotion. Therefore, we think user's emotional states to be music itself. In this paper, we try to grasp user's emotional states from music selected by users at a specific context, and we analyze the correlation between its context and user's emotional state. To get emotional states out of music, the proposed method extracts emotional words as the representative of music from lyrics of user-selected music through morphological analysis, and learns weights of linear classifier for each emotional features of extracted words. Regularities learned by classifier are utilized to calculate predictive weights of virtual music using weights of music chosen by other users in context similar to active user's context. Finally, we propose a method to recommend some pieces of music relative to user's contexts and emotional states. Experimental results shows that the proposed method is more accurate than the traditional collaborative filtering method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권12호
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• pp.3943-3959
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• 2022

People are constantly exposed to stress and anxiety environment, which could contribute to a variety of psychological and physical health problems. Therefore, it is particularly important to identify psychological stress in time and to find a feasible and universal method of stress reduction. This research investigated the influence of different music, such as relaxation music and natural rhythm music, on stress relief based on Electroencephalogram signals. Mental arithmetic test was implemented to create a stressful environment. 23 participants performed the mental arithmetic test with and without music respectively, while their Electroencephalogram signal was recorded. The effect of music on stress relief was verified through stress test questionnaires, including Trait Anxiety Inventory (STAI-6) and Self-Stress Assessment. There was a significant change in the stress test questionnaire values with and without music according to paired t-test (p<0.01). Furthermore, a model based on Transformer for stress level classification from Electroencephalogram signal was proposed. Experimental results showed that the method of listening to relaxation music and natural rhythm music achieved the effect of reducing psychological stress and the proposed model yielded a promising accuracy in classifying the Electroencephalogram signal of mental stress.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권10호
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• pp.3355-3372
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• 2022

Composing music is an inspired yet challenging task, in that the process involves many considerations such as assigning pitches, determining rhythm, and arranging accompaniment. Algorithmic composition aims to develop algorithms for music composition. Recently, algorithmic composition using artificial intelligence technologies received considerable attention. In particular, computational intelligence is widely used and achieves promising results in the creation of music. This paper attempts to provide a survey on the music generation based on the Monte Carlo (MC) algorithm. First, transform the MIDI music format files to digital data. Among these data, use the logistic fitting method to fit the time series, obtain the time distribution regular pattern. Except for time series, the converted data also includes duration, pitch, and velocity. Second, using MC simulation to deal with them summed up their distribution law respectively. The two main control parameters are the value of discrete sampling and standard deviation. Processing the above parameters and converting the data to MIDI file, then compared with the output generated by LSTM neural network, evaluate the music comprehensively.

• 한국정보통신학회논문지
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• 제11권6호
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• pp.1170-1175
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• 2007

본 논문에서는 신경회로망 알고리즘 중 하나인 backpropagation network을 이용한 악보인식 기법과 그에 필요한 악보 영상에 대한 전처리 기법을 제안한다. 전처리과정으로 이진화, 기울기 보정, 오선제거 등의 과정을 수행하여 인식에 필요한 음악 기호와 음표를 분리한다. 분리된 음악 기호와 음표들은 backpropagation 알고리즘을 사용하여 구성된 음표 인식 신경망과 비음표 인식 신경망을 통해 각각 음표와 비음표 인식과정을 거친다. 다양한 복잡도를 가진 악보를 대상으로 한 실험 및 분석 결과를 통해 제안한 악보 인식 기법의 정확도를 기술하였다.