• 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.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.53-64
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• 2013

Folksonomies have potential problems caused by synonyms, tagging level, neologisms and so forth when retrieving music by tags. These problems can be tackled by introducing the mood intensity (Arousal and Valence value) of music as its internal tag. That is, if moods of music pieces and their mood tags are all represented internally by numeric values, A (Arousal) value and V (Valence) value, and they are retrieved by these values, then music pieces having similar mood with the mood tag of a query can be retrieved based on the similarity of their AV values though their tags are not exactly matched with the query. As a prerequisite study, in this paper, we propose the mapping table defining the relation between AV values and folksonomy tags. For analysis of the association between AV values and tags, ANOVA tests are performed on the test data collected from the well known music retrieval site last.fm. The results show that the P values for A values and V values are 0.0, which means the null hypotheses could be rejected and the alternative hypotheses could be adopted. Consequently, it is verified that the distribution of AV values depends on folksonomy tags.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2019.06a
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• pp.173-175
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• 2019

Animated images formats such as WebP are highly portable graphics formats that are being used everywhere on the Internet. Despite their small sizes and duration, WebP image previews the video without watching the entire content with minimum bandwidth. This paper proposed a novel method to generate personalized WebP images in the client side using its computation resources. The proposed system automatically extracts the WebP image from climax point using music analysis. Based on user interest, the system predicts the genre using Convolutional Neural Network (CNN). The proposed method can easily integrate with streaming platforms such as YouTube, Netflix, Hulu, and others.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.1
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• pp.19-24
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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 have a tendency to build playlist for manage songs. However the manual selection of songs for creating playlist is a troublesome work. This paper proposes an auto playlist generation system considering user context of use and preferences. This system has two separated systems; 1) the mood and emotion classification system and 2) the music recommendation system. Firstly, users need to choose just one seed song for reflecting their context of use. Then system recommends candidate song list before the current song ends in order to fill up user playlist. User also can remove unsatisfied songs from the recommended song list to adapt the user preference model on the system for the next song list. The generated playlists show well defined mood and emotion of music and provide songs that the preference of the current user is reflected.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.48-51
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• 2021

The article discusses the use of modern technologies in the learning process. It has been determined that the modern period of the development of society is characterized by a strong influence of computer technologies on it, a new education system is being formed, focused on entering the world information and educational space. This process is accompanied by significant changes in the pedagogical theory and practice of the educational process associated with making adjustments to the content of learning technologies, which should be adequate to modern technical capabilities, and contribute to the harmonious entry of a teenager into the information society. Computer technologies are designed to become not an additional "makeweight" in training, but an integral part of a holistic educational process, significantly increasing its effectiveness

• ETRI Journal
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• v.38 no.3
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• pp.510-517
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• 2016

Recent developments in the field of separation of mixed signals into music/voice components have attracted the attention of many researchers. Recently, iterative kernel back-fitting, also known as kernel additive modeling, was proposed to achieve good results for music/voice separation. To obtain minimum mean square error (MMSE) estimates of short-time Fourier transforms of sources, generalized spatial Wiener filtering (GW) is typically used. In this paper, we propose an advanced music/voice separation method that utilizes a generalized weighted ${\beta}$-order MMSE estimation (WbE) based on iterative kernel back-fitting (KBF). In the proposed method, WbE is used for the step of mixed music signal separation, while KBF permits kernel spectrogram model fitting at each iteration. Experimental results show that the proposed method achieves better separation performance than GW and existing Bayesian estimators.