• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.195-202
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• 2022

The purpose of this study is to study the perceived value of consumers of traditional Chinese music training institutions, and the resulting research on the impact of customers' purchase intentions. This research refers to relevant literature on customer perceived value and customer purchase intention, and divides customer perceived value into five value dimensions: emotion, quality, experience, price and reputation. Questionnaire survey method, using SPSS analysis AMOS tool to conduct empirical research, the research results show that customer perceived value has an important positive impact on purchase intention in five value dimensions of emotion, quality, price, experience and reputation, which is a traditional music training institution. Provide reference and suggestions for improvement.

• Korean Journal of Child Studies
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• v.22 no.4
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• pp.243-255
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• 2001

The aim of the study was to investigate whether music drama activity has an affect on literacy, musical ability and physical expression in preschoolers. The subjects of this experiment were 48 five-year-old children who were selected from two kindergarten in geonbook(experimental group : 24, comparison group : 24). The children in experimental group were asked to act four young children's music drama through sixteen weeks. The children in comparison group were involved in activity referred to in storybook. The data gained from the research were analyzed using a SPSS(p=.05) computer program. The main results in this study are as follows : First, there were partly significant differences in young children's music drama activity in terms of rating of literacy. Especially, story understanding as indicated by literacy test scores in the experimental group was significantly different. There were also significant differences in young children's music drama activity in terms of rating of musical ability and physical expression.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.149-158
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• 2015

This paper proposes a subband filtering technique to the MUSIC and the ESPRIT algorithm for estimating the magnitude and frequency of the harmonics of power signal. In proposed method, the input power signal is decomposed to the odd harmonics and the even harmonics respectively by the filter bank system. The amplitude and the frequency estimation of the decomposed harmonics are carried out using the MUSIC and the ESPRIT method. Subband filtering can reduce the autocorrelation matrix size of input data, and spectrum leakage between adjacent harmonics. Therefore, this subband technique has advantage in computational cost and estimation accuracy compared to fullband MUSIC and ESPRIT. To demonstrate the performance of the method, computer simulations are performed to the synthesized input signal, and experiment results are compared in subband and fullband cases.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.438-449
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• 2015

Recognition systems for scanned or printed music scores that have been implemented on personal computers have received attention from numerous scientists and have achieved significant results over many years. A modern trend with music scores being captured and played directly on mobile devices has become more interesting to researchers. The limitation of resources and the effects of illumination, distortion, and inclination on input images are still challenges to these recognition systems. In this paper, we introduce a novel approach for recognizing music scores captured by mobile cameras. To reduce the complexity, as well as the computational time of the system, we grouped all of the symbols extracted from music scores into ten main classes. We then applied each major class to SVM to classify the musical symbols separately. The experimental results showed that our proposed method could be applied to real time applications and that its performance is competitive with other methods.

• International journal of advanced smart convergence
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• v.6 no.2
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• pp.9-15
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• 2017

According to a recent survey, most elementary and secondary school students nationwide are stressed out by their academic records. Furthermore most of high school students in Korea have to study under the great duress. Some of them who can't overcome the academic stress finalize their life by suiciding. A study has found that it is one of the leading causes of stimulating the thought of committing suicide in Korean high school students. So it is necessary to reduce the high school student's suicide rate. Main content of this research is to implement a personalized music recommendation system. Music therapy can help the student deal with the stress, anxiety and depression problems. Proposed system works as a therapist. The music choice and duration of the music is adjusted based on the student's current emotion recognized automatically from EEG. If the happy emotion is not induced by the current music, the system would automatically switch to another one until he or she feel happy. Proposed system is personalized brain music treatment that is making a brain training application running on smart phone or pad. That overcomes the critical problems of time and space constraints of existing brain training program. By using this brain training program, student can manage the stress easily without the help of expert.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.1
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• pp.41-46
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• 2010

In this paper, the DoA(Direction of Arrival) estimator using unitary MUSIC algorithm is studied. The complex-valued correlation matrix of MUSIC algorithm is transformed to the real-valued one using unitary transform for easy implementation. The eigenvalue and eigenvector are obtained by the combined Jacobi-CORDIC algorithm. CORDIC algorithm can be implemented by only ADD and SHIFT operations and MUSIC spectrum computed by 256 point DFT algorithm. Results of unitary MUSIC algorithm designed by System Generator for FPGA implementation is entirely consistent with Matlab results. Its performance is evaluated through hardware co-simulation and resource estimation.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.107-116
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• 2013

In this paper, we propose a hybrid music recommendation system combining users' listening habits and tag information in a social music site. Most of commercial music recommendation systems recommend music items based on the number of plays and explicit ratings of a song. However, the approach has some difficulties in recommending new items with only a few ratings or recommending items to new users with little information. To resolve the problem, we use tag information which is generated by collaborative tagging. According to the meaning of tags, a weighted value is assigned as the score of a tag of an music item. By combining the score of tags and the number of plays, user profiles are created and collaborative filtering algorithm is executed. For performance evaluation, precision, recall, and F-measure are calculated using the listening habit-based recommendation, the tag score-based recommendation, and the hybrid recommendation, respectively. Our experiments show that the hybrid recommendation system outperforms the other two approaches.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.12
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• pp.3016-3022
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• 2013

In this paper, we propose a novel design and implementation method to detect musical plagiarism which can provide human experts evidences to decide plagiarism using cutting-edge information technologies and thereby can solve exhaustive disputes on cases of musical plagiarism when the cases are decided by human experts' emotional preferences. We first search digital music elements to analyze music source and examine how to use these in plagiarism analysis using IT techniques. Therefore we designed music plagiarism analysis system by using MusicString which is supported in JFugue and construct AST to manipulate music plagiarism analysis efficiently.

• Journal of the Korea Society of Computer and Information
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• v.28 no.1
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• pp.161-170
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• 2023

In this study, based on existing studies on music streaming services and e-services, the selection factors for music streaming platforms were derived, and the AHP technique was implemented to calculate the importance of each factor. As a result of this study, economic feasibility was found to be the most important factor among security, economic feasibility, informativeness, convenience, and responsiveness, which are the first-step selection factors of music streaming platforms. As a result of synthesizing the weights of the first and second factors, reasonable price was found to be the most important factor. Finally, an additional analysis was conducted to determine whether there was a difference in importance between the selection factors of the music streaming platform according to gender and age. Through this study, it will be possible to figure out the factors that consumers consider most important when using a music streaming platform.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.01a
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• pp.291-292
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• 2016

비만의 인구가 증가함에 따라 비만을 치료하는 방법에 대한 관심도 높아지고 있다. 본 논문에서는 운동 시 열량 소비에 더욱 도움을 주는 음악장르를 알아보는데 목적을 두었으며 6가지 장르의 음악을 선정하여 10분간 사이클을 타며 칼로리를 비교분석 하였다. 평균적으로 디스코, 댄스, 힙합음악을 들을 때 더 많은 칼로리를 소모하였다.