• Journal of IKEEE
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• v.13 no.2
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• pp.159-166
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• 2009

So far, many researches have been done to retrieve music information using static classification descriptors such as genre and mood. Since static classification descriptors are based on diverse content-based musical features, they are effective in retrieving similar music in terms of such features. However, human emotion or mood transition triggered by music enables more effective and sophisticated query in music retrieval. So far, few works have been done to evaluate the effect of human mood transition by music. Using formal representation of such mood transitions, we can provide personalized service more effectively in the new applications such as music recommendation. In this paper, we first propose our Emotion State Transition Model (ESTM) for describing human mood transition by music and then describe a music classification and recommendation scheme based on the ESTM. In the experiment, diverse content-based features were extracted from music clips, dimensionally reduced by NMF (Non-negative Matrix Factorization, and classified by SVM (Support Vector Machine). In the performance analysis, we achieved average accuracy 67.54% and maximum accuracy 87.78%.

• Journal of Korean Library and Information Science Society
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• v.50 no.1
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• pp.105-124
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• 2019

This study was to suggest the improvement and the application of KDC 6 for classifying children's books by the literature review and survey. First, it was suggested to shorten the classification numbers by the divisions and subdivisions and to expand the classification numbers by sub-subdivisions according to the library-specific classification policy, using the subject statistics of the children's books held by 20 public libraries affiliated in Seoul Metropolitan Office of Education and representative C libraries. Second, the knowledge picture books and the fairy tales were suggested to be classified according to its subject, and the fairy tales in each country were suggested to be classified by adding sub-subdivisions and genre subdivisions. Third, it was suggested to shelve by collection and location codes that were distinguished by the ages and the reading level for user, to prepare a standard guideline for shelving, and to implement the regular user education about the classification system. This study could contribute to the development of the KDC abridged version for children's books in the future.

• Journal of Korea Game Society
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• v.11 no.5
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• pp.43-52
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• 2011

This study assumed the competitive structure based on genre classification for Korean online game market, and carried out the analysis on the degree and characteristics of the competition that appear differently in each sub market classified according to the genre. Analysis result can be summarized as follows. (1) In the view of the total play time, the proportions of the online games in each genre were appeared in the order of "RPG > FPS/RTS > Sports > Arcade/Poker > Go-stop > Board/Racing". (2) In the view of the shares in the play time, the degree of competition between games in the sub market structure of the online games according to the genre was found in the order of "RTS > Racing > FPS/Sports > Go-stop > Poker > Arcade/Board > RPG". The evaluation and interpretation of the high and low of the competition degree are as follows. As the degree of competition is higher, (RTS > Racing > ${\cdots}$) the royalty and the ratio of reusing of the users for the game is lower, and on the other hand, the tendency of replacing and using appeared higher, so the market entry barrier for a new game can be evaluated to be low. Reversely, as the degree of competition is lower, (${\cdots}$ > Arcade/Board > RPG) the royalty and the ratio of reusing of the users for the game is higher, and on the other hand, the tendency of replacing and using appeared lower, so the market entry barrier for a new game can be evaluated to be high.

• Journal of Korea Multimedia Society
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• v.10 no.5
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• pp.559-568
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• 2007

Anchor-person scene detection is of significance for video shot semantic parsing and indexing clues extraction in content-based news video indexing and retrieval system. This paper proposes an efficient algorithm extracting anchor ranges that exist in sports news video for unit structuring of sports news. To detect anchor person scenes, first, anchor person candidate scene is decided by DCT coefficients and motion vector information in the MPEG4 compressed video. Then, from the candidate anchor scenes, image processing method is utilized to classify the news video into anchor-person scenes and non-anchor(sports) scenes. The proposed scheme achieves a mean precision and recall of 98% in the anchor-person scenes detection experiment.

• Journal of Korea Game Society
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• v.21 no.4
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• pp.73-84
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• 2021

In this paper, we propose an analysis method that categorizes the Core that essential behaviors in game, the Primary that solves the game problem, and the Secondary that helps the Core and the Primary. The proposed method can analyze the genre similarity and characteristics of the game, the richness of the content, and the proficiency level of the game. case study were conducted to confirm whether the analysis items were consistent with the objective game experience. The results of this study are expected to be helpful in improving game design ability.

• Journal of the Korean Institute of Landscape Architecture
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• v.48 no.5
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• pp.42-58
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• 2020

The purpose of this study is to classify types of urban flower landscape. As a result of the study, first, through literature and case review, it was found that the four elements of place element, form element, natural element, artificial element, should be included in the sentence and key expression for defining the concept of flower landscape. In contemplating these four elements, a newly reconstructed concept of flower landscape was presented. This is expected to be the basis for the flower landscape integration theory. Second, flower landscape was defined as a genre and a unit of urban landscape. In addition, in order to build a system of flower landscape as a specialized area, after considering the concept, characteristics, and functions of a large category of urban landscape, its hierarchical categories with flower landscape were newly arranged. Thus, the flower landscape as an urban landscape was suggested. Third, in order to provide rational selection materials to consumers through type classification, related theories were investigated by expanding not only to the flower field, but also to the urban planning and urban ecology fields. 41 elements for the type classification were extracted, and 4 core elements were derived through the clustering process. Based on the 4 elements as the classification criteria, through the opinion verification from the FGI with experts, 9 types of middle-classification and 30 types of small-classification were derived. As a follow-up research suggestion, if a valid type is additionally established through a monitoring in the type application process, and more specified application types are developed and organized by expanding second-level classification hierarchy to the third-level hierarchy, this will lead to great studies improving the system of the types.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.80-81
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• 2016

본 논문에서는 딥 러닝을 이용한 오디오 장르 분류 기술을 제안한다. 장르는 music, speech, effect 3가지로 정의하여 분류한다. 기존의 GMM을 이용한 장르 분류 기술은 speech의 인식률에 비해 music과 effect에 대한 인식률이 낮아 각 장르에 대한 인식률의 차이를 보인다. 이러한 문제를 해결하기 위해 본 논문에서는 딥 러닝을 이용해 높은 수준의 추상화 과정을 거쳐 더 세분된 학습을 진행한다. 제안한 방법을 사용하면 미세한 차이의 특성까지 학습해 장르에 대한 인식률의 차이를 줄일 수 있으며, 각 장르에 대해 GMM을 이용한 오디오 장르 분류보다 높은 인식률을 얻을 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2014.06a
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• pp.3-4
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• 2014

음악 장르 분류 분야에서는 다양한 특징을 모아서 특징 벡터를 만들고 이를 support vector machine (SVM)와 같은 분류기에 입력하는 시스템이 주로 사용되고 있다. 이 논문에서는 거리 함수 학습를 음악 장르 분류를 위한 특징 벡터의 간소화에 적용하였다. 여러 거리 함수 학습 방법 중 하나의 방법을 선택하고, 기존의 논문들에서 사용되었던 특징 셋을 활용하여 기존 특징 셋에 대해서 성능을 떨어뜨리지 않으면서 특징 셋의 길이를 줄일 수 있는지 살펴본다. 우리의 실험에서는 168차원의 특징 셋을 10차원까지 줄였는데, 이 경우 분류 정확도가 2% 이내로 저하되었다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2016.06a
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• pp.90-91
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• 2016

본 논문에서는 스펙트로그램을 이용한 딥 러닝 기반의 오디오 장르 분류 기술을 제안한다. 기존의 오디오 장르 분류는 대부분 GMM 알고리즘을 이용하고, GMM의 특성에 따라 입력 성분들이 서로 직교한 성질을 갖는 MFCC를 오디오의 특성으로 사용한다. 그러나 딥 러닝을 입력의 성질에 제한이 없으므로 MFCC보다 가공되지 않은 특성을 사용할 수 있고, 이는 오디오의 특성을 더 명확히 표현하기 때문에 효과적인 학습을 할 수 있다. 본 논문에서는 딥 러닝에 효과적인 특성을 구하기 위하여 스펙트로그램(spectrogram)을 사용하여 오디오 특성을 추출하는 방법을 제안한다. 제안한 방법을 사용한면 MFCC를 특성으로 하는 딥 러닝보다 더 높은 인식률을 얻을 수 있다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.29-30
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• 2017

본 논문에서는 인간의 청각 기관을 모델링 한 스파이크그램 (spikegram)을 이용한 심층 신경망 기반의 음악 장르 분류 기술을 제안한다. 분류 대상은 GTZAN 데이터 세트의 10개 장르로 정의한다. 본 논문에서는 청각 기관의 인식 방법을 모델링한 방법을 이용하여 스파이크그램을 구하고, 스파이크그램에서 새로운 특성 벡터를 추출하는 방법을 제안한다. 제안하는 방법을 통해 심층 신경망에 적합한 특성 벡터를 구하고 이렇게 구한 특성 벡터로 신경망을 학습시켜 기존에 사용하던 다양한 방법들보다 높은 성능을 얻을 수 있다.