• Journal of Korea Multimedia Society
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• v.21 no.1
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• pp.45-50
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• 2018

This research paper describes how to summarize music lyrics using the TextRank algorithm. This method can summarize music lyrics as important lyrics. Therefore, we recommend music more effectively than analyzing the number of words and recommending music.

• Journal of the Korea Society of Computer and Information
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• v.20 no.11
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• pp.97-104
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• 2015

In this study, the investigator distributed total 100 questionnaires by childcare center teachers in final analysis. The purposes of the study were to examine teachers' perceptions of early childhood music education provided by childcare centers or kindergartens and also the difficulties they experienced with the instruction of early childhood music education and thus provide basic data to develop an educational program for early childhood teachers and search for a direction in early childhood music education in the field of education. The findings were summarized as follows: Both kindergarten and childcare center teachers showed a high perception level of need for and importance of early childhood music education. The teachers thought that early childhood music education facilitated the development of young children across all areas and promoted their pleasure and expressive abilities. Providing music education, the teachers faced difficulties due to the lack of musical knowledge, basic musical skills, and experiences with and confidence in musical activities. They also felt a need for teacher education.

• Journal of the Korea Society of Computer and Information
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• v.13 no.3
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• pp.99-106
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• 2008

Recently, digital music retrieval is using in many fields (Web portal. audio service site etc). In existing fields, Meta data of music are used for digital music retrieval. If Meta data are not right or do not exist, it is hard to get high accurate retrieval result. Contents based information retrieval that use music itself are researched for solving upper problem. In this paper, we propose Same music recognition method using similarity measurement. Feature data of digital music are extracted from waveform of music using Simplified MFCC (Mel Frequency Cepstral Coefficient). Similarity between digital music files are measured using DTW (Dynamic time Warping) that are used in Vision and Speech recognition fields. We success all of 500 times experiment in randomly collected 1000 songs from same genre for preying of proposed same music recognition method. 500 digital music were made by mixing different compressing codec and bit-rate from 60 digital audios. We ploved that similarity measurement using DTW can recognize same music.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.91-99
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• 2010

Recently, recognizing user resource for personalized service has been needed in digital content service fields. Especially, to analyze user taste, recommend music and service music related information need recognition of user music file in case of online music service. Music related information service is offered through recognizing user music based on tag information. Recognition error has grown by weak points like changing and removing of tag information. Techniques of content based user music recognition with music signal itself are researched for solving upper problems. In this paper, we propose user music recognition on the internet by extracted feature from music signal. Features are extracted after suitable preprocessing for structure of content based user music recognition. Recognizing on music server consist of feature form are progressed with extracted feature. Through this, user music can be recognized independently of tag data. 600 music was collected and converted to each 5 music qualities for proving of proposed recognition. Converted 3000 experiment music on this method is used for recognition experiment on music server including 300,000 music. Average of recognition ratio was 85%. Weak points of tag based music recognition were overcome through proposed content based music recognition. Recognition performance of proposed method show a possibility that can be adapt to online music service in practice.

• Journal of Information Technology Services
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• v.7 no.3
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• pp.47-70
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• 2008

File sharing technology is the most popular methodology through which consumers gain music from online. However, music file sharing and free downloads of music have caused terrible recession of traditional music industry. The purpose of this paper is to develop the underlying theory for understanding the acceptance of music file sharing technology and empirically test our theoretical model. We develop extended TAM model and explore the influencing factors on the user acceptance of music file sharing technology. Our study delivers a better understanding on consumers’ attitudes towards music downloads. By understanding the fundamental characteristics of technology that makes consumers enthusiastic, traditional music industry will gain managerial implications.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.306-309
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• 2000

We developed an electronic music glove (EMG) system that could play musical scores in real time processing. The EMG system interfaces with the signal coming from the controller to the sound card in the computer. The computer, according to the status of the finger and foot switches, generates the signals to the speaker systems using the application C++ program by making use of MIDI message. The EMG systems can control up to several octave notes and duration of sound, and several musical performance expressions such as chorus, reverberation, rhythm, and volume. Finally, our EMG could play the performance of simple music depending on the choice of any kind of musical instruments in the sound card in computer systems.

• Journal of the Korea Society of Computer and Information
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• v.22 no.1
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• pp.77-87
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• 2017

In this paper, we propose an analysis of the sound source of virtual orchestral music in detail by using the production technology of sampling and playback, and technical experiments and verification for the most advanced Hollywood Library series of timbre and the timbre production and control technology of Vienna instrument Pro and other softwares, to find their solutions and come up with the best timbre production method for orchestral music from the perspective of the specific design ideas of the productions of virtual orchestral instruments. The so-called virtual orchestral instruments are non-real orchestral instruments, which are based on the imitation of timbre of real orchestral instruments, processed and synthesized through the scientific and technological means, and produced by the use of electronic equipments or computers. The study of virtual timbre production technology is very important, and it is like the study of composers' creative techniques. Not only scientific and advanced timbre production methods can be obtained from the study, but also new ways of future timbre production are expected to be explored.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.115-116
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• 2016

PC용 작곡 프로그램의 보편화에도 일반인들이 음악제작을 어려워하는 이유는 악기를 다룬 경험의 부족, 음악 또는 작곡의 지식부족, MIDI 소프트웨어 학습에 대한 두려움 등이다. 본 논문에서는 이러한 문제를 해결하기 위한 기술로 MIDI를 전혀 모르고, 악기를 다룬 경험이 부족하고 또한 악보조차 읽지 못하더라도 음악의 제작 및 작곡, 편곡이 가능한 효과적인 일반 텍스트 기반 음악 표기법을 제안한다. 이를 통해 음악을 좋아하지만 자신의 음악을 제작하지 못했던 많은 일반인들에게 음악을 제작, 보급하는데 큰 도움이 될 뿐 아니라 타인과의 협업을 통해 우수한 품질의 음악을 제작할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.204-207
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• 2013

Music genre classification is an essential component for music information retrieval system. There are two important components to be considered for better genre classification, which are audio feature extraction and classifier. This paper incorporates two different kinds of features for genre classification, timbral texture and rhythmic content features. Timbral texture contains several spectral and Mel-frequency Cepstral Coefficient (MFCC) features. Before choosing a timbral feature we explore which feature contributes less significant role on genre discrimination. This facilitates the reduction of feature dimension. For the timbral features up to the 4-th order central moments and the covariance components of mutual features are considered to improve the overall classification result. For the rhythmic content the features extracted from beat histogram are selected. In the paper Extreme Learning Machine (ELM) with bagging is used as classifier for classifying the genres. Based on the proposed feature sets and classifier, experiment is performed with well-known datasets: GTZAN databases with ten different music genres, respectively. The proposed method acquires the better classification accuracy than the existing approaches.

• Journal of The Korean Association of Information Education
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• v.6 no.1
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• pp.42-52
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• 2002

For an effective listening to music, a repeated listening to music should be possible without any restriction of time and space as well as to music for a music class and the various multi-media learning materials for listening to music is required. However, an individualized and repeated listening is difficult and an individualized listening materials are lacking. To solve these problems, learning system for listening to music in elementary school sharing learning materials required for it and including learning factors of listening to music is required. Therefore, this study analyzes learning factors of listening to music in elementary school and the learning system for listening to music is designed and developed to make elementary students have their own active listening to music and feel achievement and confidence by teacher's evaluation of their writings after listening to music and experience a joyful musical activity. And it is applied to elementary school.