• Journal of the Korea Society of Computer and Information
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• v.20 no.10
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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.

• Korean Journal of Childcare and Education
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• v.15 no.2
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• pp.125-144
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• 2019

Objective: The purpose of this study was to examine the effects of early childhood teachers' music attitude and emotional leadership on their music teaching efficacy in the music education. Methods: 301 early childhood teachers answered the music attitude scale, music teaching efficacy belief instrument, emotional leadership scale, and questionnaire for socio-demographic characteristics and music experiences. Data were analyzed by t-test, one-way ANOVA, Pearson's productive correlation analysis and hierarchical regression analysis. Results: First, early childhood teachers had a higher music teaching efficacy in case of at least 10 years of teaching experiences period, having a post-graduate degree, having a music training experience, enjoying learning musical instruments and singing and listening to music during regular music lessons, and having a long music training experience. Similar results were derived from the subfactors of music teaching efficacy. Second, there were positive correlations(r=.172-.659, p < .001) in the total and subfactors scores among early childhood teachers' music attitude, emotional leadership, and music teaching efficacy. Lastly, early childhood teachers' music attitude and their emotional leadership were explained at 39~52 percent for their music teaching efficacy. Conclusion/Implications: This study suggests that it is important for early childhood teachers' perception of their belief, knowledge and feeling about music education.

• Fashion & Textile Research Journal
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• v.16 no.3
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• pp.354-365
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• 2014

This study investigates the positioning of emotional language imagesin popular music genres for developing textile print design ideas. Auditory and synaesthetic imagery were employed to deduct emotional language imageries from popular music genres and analyze differences in emotional language imageries according to popular music genres. Six genres of popular music were selected as stimulus and a survey was conducted to analyze emotional language imagery differences and similarities depending on popular music genres. The results of this study were: The results of the factor analysis and the reliability test on emotional language imagery showed factorial structures that include Lyrical-Feminine, Intense-Masculine, Euphoric-Active, Gloomy-Melancholy, Abstruse-Sophisticated, and Addictive-Continuous. The results of the mean scores of emotional language imagery of each popular music genre showed that respondents tended to perceive that ballad and new age music are similar and hip-hop & rap, dance, and metal-rock are similar. Based on the multidimensional scaling analysis, new age positioned Lyrical-Feminine, metal-rock positioned Intense-Masculine, dance music positioned Euphoric-Active, and ballad positioned Gloomy-Melancholy. This study provides elementary resources to inspire innovative textile prints designed through different characteristics of emotional language imagery according to each popular music genre.

• The Journal of the Korea Contents Association
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• v.21 no.8
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• pp.571-579
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• 2021

This study aims to identify the relationship between ADHD trait and game addiction among high school students, and to examine the double mediating model of hardiness and emotional use of music on that relationship. Participants were 254 male and female high school students. World Health Organization ADHD Self-Report Scale, Short Form of Hardiness Questionnaire, Use of Music Inventory, and Internet Gaming Use-Elicited Symptom Screen were used for this study. PROCESS Macro 3.5 Model 6 was used to analyse a double mediating effect. Results revealed that ADHD trait was negatively correlated with hardiness of high school students, and positively correlated with emotional use of music and game addiction. And, hardiness of high school students was positively correlated with emotional use of music and negatively correlated with game addiction, whereas emotional use of music was negatively correlated with game addiction. It was found that hardiness and emotional use of music were sequentially mediating ADHD trait and game addiction among high school students. These findings suggest that hardiness and emotional use of music play some special roles in the path in which adolescents' ADHD trait affects game addiction.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1464-1485
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• 2021

Emotional style of multimedia art works are abstract content information. This study aims to explore emotional style transfer method and find the possible way of matching music with appropriate images in respect to emotional style. DCNNs (Deep Convolutional Neural Networks) can capture style and provide emotional style transfer iterative solution for affective image generation. Here, we learn the image emotion features via DCNNs and map the affective style on the other images. We set image emotion feature as the style target in this style transfer problem, and held experiments to handle affective image generation of eight emotion categories, including dignified, dreaming, sad, vigorous, soothing, exciting, joyous, and graceful. A user study was conducted to test the synesthesia emotional image style transfer result with ground truth user perception triggered by the music-image pairs' stimuli. The transferred affective image result for music-image emotional synesthesia perception was proved effective according to user study result.

• Journal of the Korea Society of Computer and Information
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• v.18 no.5
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• pp.43-52
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• 2013

In recent years, a large amount of music content is distributed in the Internet environment. In order to retrieve the music content effectively that user want, various studies have been carried out. Especially, it is also actively developing music recommendation system combining emotion model with MIR(Music Information Retrieval) studies. However, in these studies, there are several drawbacks. First, structure of emotion model that was used is simple. Second, because the emotion model has not designed for Korean language, there is limit to process the semantic of emotional words expressed with Korean. In this paper, through extending the existing emotion model, we propose a new emotion model KOREM(KORean Emotional Model) based on Korean. And also, we design and implement ontology using emotion model proposed. Through them, sorting, storage and retrieval of music content described with various emotional expression are available.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.707-710
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• 2009

In this paper, we introduce an interface for exploring music using emotional model. First, we survey arousal-valence factors of various music and calculate a correlation between audio fefatures of music and arousal-valence factors to build an AV model. Then, various music is aligned and arranged using the AV model and the user can explore music in this interface. To select the desired music more intuitively, we introduce new fade in/out function based on the location of the user's mouse point. We also offer several mode of selecting music so user can explore music using most suitable mode of interface. With our interface, the user can find the emotionally desired music more easily.

• Fashion & Textile Research Journal
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• v.17 no.4
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• pp.524-540
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• 2015

This study develops textile pattern design ideas created through the visualization of music. Methods of auditory and synesthesia were employed to analyze various attributes of popular music genres and appoint language image, shape image, and color image to obtain their interrelationships. This study provides data that can be used to express emotional images on textile print pattern designs. This research used different genres of popular music as stimuli. The language image was extracted and introduced to the overall color scheme; in addition, the color image was verified. The analysis of the color image was executed by applying it with the color set image scale of I.R.I colors. Then, the color image of the target genre of popular music was examined and analyzed through a color tone system. The preference in shape image was realized through visual images based on basic principles of points, lines, and sides composition; subsequently, an analysis of the emotional image of popular music followed. An examination of the emotional images of different popular music genres have led to the discovery that language image, color image, and shape image all share a common emotional image. There was also a realization that similarity and interrelationship exists in language, color, and shape images experienced by listening to popular music.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.652-655
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• 1997

An exploration for an intelligence paradigm has been delineated. Artificial intelligence and artificial life paradigms seem to fail to show the whole picture of human intelligence. We may understand the human intelligence better by adding the emotional part of human intelligence to the intellectual part of human intelligence. Emotional intelligence is investigated in terms of composing machine as a modern abstract art. Various algorithmic composition and performance concepts are currently being investigated and implemented. Intelligent mapping algorithms restructure the traditional predetermined composition algorithms. Music based on fractals and neural networks is being composed. Also, emotional intelligence and aesthetic aspects of Korean traditional music are investigated in terms of fractal relationship. As a result, this exploration will greatly broaden the potentials of the intelligence research. The exploration of art in the view of intelligence, information and structure will restore the balanced sense, of art and science which seeks happiness in life. The investigations of emotional intelligence will establish the foundations of intelligence, information and control technologies.

• Journal of Internet Computing and Services
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• v.23 no.2
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.