• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Journal of Gifted/Talented Education
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• v.26 no.4
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• pp.587-609
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• 2016

The purpose of this study is to analysis the cognitive frame of the expert on musically talented in the Pop music audition program. Consequently, this article found that each expert has difference priority. Myself(PA), voice(YA), tune(YO) are used frequently. In comparison with standard frame, all experts refer to creative area in priority. But, they lack in motive area. Also, there are areas which don't treat in standard frame. And to conclude, pop music audition program has creative and personal characteristic. If not, it doesn't give prominence to differentiated messages and last in the global market. Based on the result of this study, this paper suggests the following: Firstly, it needs devices that support areas which fall short of selection. Secondly, it requires new frame which modifies the details of standard frame.

• The Journal of the Korea Contents Association
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• v.16 no.6
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• pp.168-182
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• 2016

The domestic market for performances and concerts is rapidly growing. However, despite of the various efforts to attract audience, the market still struggles in securing consumers for classical music performances. In this context, the following thesis first explores the factors that determine the choice of performances through open-ended questions to categorize the types of consumers based on the results. Then, effects of different factors of choices for each type of consumer are studied to find out how these factors affect the consumers' purchasing decisions. As a result, 35 factors out of the 40 factors were ultimately confirmed as the factors that determine the consumers' choice for purchasing classical music performances. Then, the 35 factors were classified into seven categories. Moreover, an empirical analysis showed that personal factors, factors regarding contents of the performance, information factors, environmental factors and marketing factors had significant effects on the consumers' purchasing decisions. The degree of influence of the factors for each type of audience varied. This study conclusively seeks to contribute to developing a more thorough marketing strategies for performance arts institutions and performance venues.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.591-600
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• 2020

The decrease in the number of school-age population, the introduction of university evaluation and educational capacity enhancement projects have made the enrollment rate and employment rate important indicators. Accordingly, universities are making great efforts to improve the competitiveness of their universities by increasing students' major satisfaction. The purpose of this study is to find out whether art and music students major selection motivsion of affects major satisfaction for career preparation behavior. For this survey, students of arts and physical education at K University in Gyeonggi-do were surveyed and 197 questionnaires were used as analysis data. As a result of the study, it was found that major selection motivation had a significant static effect on career preparation behavior and major satisfaction, and major satisfaction was analyzed to have a complete mediating effect on the effect of major selection motivation on career preparation behavior. With on the results of this study, in the future, universities will need an efficient curriculum to enhance students' majors satisfaction. To that end, we should develop the theoretical and practical curriculum so that students can actively participate, operate programs such as operation of comparative subjects and on-demand education, and raise the level of education. To this end, the interest and active support of the industry-academia-research are required.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.38-48
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• 2020

The domestic AI speaker market is growing into a full-fledged early audience market beyond the innovative consumer market with 3 million domestic supply units at the end of 2018, but the reality is that for various reasons, we are not satisfied with the use. There are many previous papers on AI Speaker, but the majority of research so far tends to be biased towards the acceptance of the device's own performance. Many changes are being made, such as OTT providers trying to secure the market through collaboration with AI speaker providers. This study tried to identify the priorities for content services, which can be another major selection factor for AI speakers, excluding the factors of unsatisfactory technology. First, this study identified the priorities among AI speaker selection factors using AHP (Analytic Hierarchy Process), based on the AI speaker selection factors derived through literature research. The most important hierarchical factor are Concierge Service, Education Service, and Entertainment Service order in AI speaker selection, and the primary content among the individual factors was the one that ranked weather/temperature/fine dust (11.6%) and child caring content was in the second place (10.8%), and then music service was in the third place (9.8%). The three top priorities were derived from the items in the top tier 1, 2 and 3 priorities. Of the total 15 individual services, 6 sub-layers of Concierge Service (weather/temperature/fine dust, news, voice schedule notification) and Education Service (foreign language, toddler, reading books) were in the top 8, and two of the Entertainment Service Music service and movie service ranked third and sixth.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.369-370
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• 2009

Robot이 음악에 맞춰 어떤 행동을 하기 위해선 먼저 Acoustic을 이해 할 수 있는 인지 능력이 필요하며 인지한 음악적 내용을 Dance Motion에 가깝게 Action을 표현할 수 있어야 한다. 본 논문에서는 신호처리와 기계학습을 사용하여 음악의 Tempo를 Tracking하고 이것을 참고하여 행동 Pattern을 결정하는 Dance Robot System을 소개한다.

• The Journal of the Korea Contents Association
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• v.8 no.12
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• pp.109-117
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• 2008

This research analyzed which average comparisons and differences between groups' view motive and selection for information sources, product properties with theater animation spectator. Based on view frequency, each of groups' organization were classified to heavy, occasional, and thinly viewers. As average comparison analysis result, firstly, view motive appeared in order to "want to see animation", "to spend time and leisure activity", "to enjoy fun activity", and "because of others canvassing or recommendation", etc. Secondly, view selection for information source appeared in order to "rumor circumstance or reputation", "theater or TV previews", "internet evaluation and grade", etc. At last, view selection for practical property appeared in order to "story", "character", "special effects", "background music", "background art", "director/directing", "manufacturer/nation", and "dubbing of artist". As difference between group result, view motive and selection for product properties appeared significant differences between each of group. To the contrary, view selection for information sources did not appeared significant differences between each of group.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10C
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• pp.965-974
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• 2007

In this paper, we proposed the feature selection algorithm for multi-class genre classification. In our proposed algorithm, we developed GMM separation score based on Gaussian mixture model for measuring separability between two genres. Additionally, we improved feature subset selection algorithm based on sequential forward selection for multi-class genre classification. Instead of setting criterion as entire genre separability measures, we set criterion as worst genre separability measure for each sequential selection step. In order to assess the performance proposed algorithm, we extracted various features which represent characteristics such as timbre, rhythm, pitch and so on. Then, we investigate classification performance by GMM classifier and k-NN classifier for selected features using conventional algorithm and proposed algorithm. Proposed algorithm showed improved performance in classification accuracy up to 10 percent for classification experiments of low dimension feature vector especially.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.682-685
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• 2005

In this research, how the evaluation of the spacial image influenced by the environmental friendly elements included in the visual information, and how the selection of the sound changed depending on the characteristics of spatial image by the 40 subjects were carried out. Vast tracts of green land and the waterfront were highly preferred and impressive than the other spaces. The green music, signal with water sound and bird chirping sound were highly scored. In the frequency characteristics of the factors, the first factor was artificial sound(high at the low frequency band), the second was natural sound(uniform at all frequency band) and the third was water sound(high at the middle and high frequency band over 500Hz). This shows that the proposal of the sound which has the frequency characteristics fit to the spacial image should be selected for the soundscape of the target space.

• Korea Science and Art Forum
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• v.37 no.3
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• pp.125-134
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• 2019

This study was conducted to organize a program to classify and analyze the characteristics of images for the automation of background music selection in the video content production process. The results and contents of the study are as follows: video characteristics are selected as subject category, emotion, pixel motion speed, color, and character material. Subject categories and feelings were extracted using Microsoft's Azure Video Indexer, Pixel Movement Speed was an Optional flow, Color was an Image Histogram for Image, and character materials was CNN(Convolutional Neural Network). The results of this study are significant in that video analysis was conducted to match background music in the recent content production process of 'Internet One-person Broadcasting Creators'.