• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.

• The Transactions of the Korea Information Processing Society
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• v.7 no.12
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• pp.3762-3774
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• 2000

The advances of computer hardware and information processing technologies raise the needs of multimedia information retrieval systems. Up to date. multimedia information systems have been developed for text information and image information. Nowadays. the multimedia information systems for video and audio information. especially for musical information have been grown up more and more. In recent music information retrieval systems. not only the information retrieval based on meta-information such like composer and title but also the content-based information retrieval is supported. The content-based information retrieval in music information retrieval systems utilize the similarity value between the user query and the music information stored in music database. In tbis paper. hence. we developed a similarity computation algorithm in which the pitches and lengths of each corresponding pair of notes are used as the fundamental factors for similarity computation between musical information. We also make an experiment of the proposed algorithm to validate its appropriateness. From the experimental results. the proposed similarity computation algorithm is shown to be able to correctly check whether two music files are analogous to each other or not based on melodies.

• Journal of Korean Library and Information Science Society
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• v.40 no.1
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• pp.275-296
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• 2009

The purpose of this study is to investigate bibliographic control of printed music that is especially far-off the bibliographic control activities. In this study, therefore, the production and distribution channels of printed music and their publication situation are investigated. Then the development of bibliographic control of printed music is examined. And also, domestic, foreign, and international bibliographic control activities of printed music are analyzed. Finally, the special issues to be considered in our bibliographic control of printed music are presented.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.9
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• pp.650-657
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• 2006

The music is a part of our daily life in these days. And when the people listen to the music, they are affected by the context. However, previous researches on the music recommendation system have the problem that they didn't consider the proper contextual information efficiently. They only used the content-based filtering or the method to use musical metadata (genre, artist, etc.). Recently, there are some researches about the music recommendation system which applies the status(temperature, humidity, etc.) of environments. But, it is difficult to be accepted by the contextual information. Therefore, we propose the music recommendation system that is dynamically applied by the contextual information as well as the metadata in the previous researches. And the system can provide users with the music that they want to listen to, and then the users can be more satisfied. Also, the services can be improved by the feedback of the users. In order to solve this problem, the context-information for selecting a music list is defined and the music recommendation system is designed by using the content-based filtering method. The system is suitable for the user's taste and the context. The music recommendation system we are proposing uses an OSGi framework in the home network. As a result, the satisfaction of users and the quality of services will be improved more efficiently by supporting the mobility of services as well as the distributed processing.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.75-78
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• 2018

Recently, the amount of music that can be heard is increasing exponentially due to the growth of the digital music market. Because of this, online music service users have had difficulty choosing their favorite music and have wasted a lot of time. In this paper, we propose a recommendation technique to minimize the difficulty of selection and to reduce wasted time. The proposed technique uses an item - based collaborative filtering algorithm that can recommend items without using personal information. For more accurate recommendation, the user's preference is predicted by using the metadata of the music source and the top-N music with high preference is finally recommended. Experimental results show that the proposed method improves the performance of the proposed method better than it does when the metadata is not used.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.250-255
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• 2006

In digital multimedia society, we usually use the digital sound music ( Mp3, wav, etc.) system instead of analog music. In the middle of generating or recording and transmitting, if we embed the digital code which is useful to music information, we can easily select as well as classify the music title by using Mp3 player that embedded sound source classification system. In this paper, sound source classification system which could be classify and search a music informations by way of user friendly scheme is implemented. We performed some experiments to testify the validity of proposed scheme by using implemented system.

• Journal of the Korea Society of Computer and Information
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• v.18 no.8
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• pp.43-52
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• 2013

The purpose of this paper is researching musical elements to analyze plagiarism between two sources. We first search digital music elements to analyze music source and examine how to use these in plagiarism analysis using compiler techniques. In addition we are used open source Java API JFugue to process complex MIDI music data simply. Therefore we designed music plagiarism analysis system by using MusicString which is supported in JFugue and construct AST after investigate MusicString's syntax processing elements to manipulate music plagiarism analysis efficiently. So far music plagiarism analysis is evaluated emotionally and subjectively. But this paper suggests first step to build plagiarism analysis systemically. If this research is well utilized, this is very meaningful to standardize systemically which music is plagiarized or not.

• 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 Information Technology Applications and Management
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• v.24 no.3
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• pp.63-72
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• 2017

The purpose of this study is to investigate the relationship between application attractiveness and loyalty to mobile music applications. The application attractiveness is operationalized into four dimensions: richness of music contents, music app design quality, music app functionality, and promotion. The hypotheses are postulated and tested using a sample of 370 student respondents from Henan Polytechnic University and Henan Institute of Technology, China. The result shows that there is a positive correlation between loyalty and three application attractiveness aspects: richness of music contents, music app design quality and music app functionality. Based on the results of this study, the research put forward constructive suggestions about improving mobile music application loyalty. Finally, several conclusions, managerial suggestions, limitations and future research are proposed.