• 한국의류산업학회지
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• 제16권3호
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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.

• Journal of information and communication convergence engineering
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• 제15권1호
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• pp.43-48
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• 2017

Movie ratings are crucial for recommendation engines that track the behavior of all users and utilize the information to suggest items the users might like. It is intuitively appealing that information about the viewing preferences in terms of movie genres is sufficient for predicting a genre of an unlabeled movie. In order to predict movie genres, we treat ratings as a feature vector, apply a Bernoulli event model to estimate the likelihood of a movie being assigned a certain genre, and evaluate the posterior probability of the genre of a given movie by using the Bayes rule. The goal of the proposed technique is to efficiently use movie ratings for the task of predicting movie genres. In our approach, we attempted to answer the question: "Given the set of users who watched a movie, is it possible to predict the genre of a movie on the basis of its ratings?" The simulation results with MovieLens 1M data demonstrated the efficiency and accuracy of the proposed technique, achieving an 83.8% prediction rate for exact prediction and 84.8% when including correlated genres.

• 한국게임학회 논문지
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• 제6권2호
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• pp.23-32
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• 2006

컴퓨터게임의 서사 개념을 이용하여 장르를 구분하는 틀을 제시하려 하였다. 게임의 서사에 대해서는 아직 논란이 많은 편이기는 하지만, 전통적인 개념으로서 게임 연구에 있어서도 유용하게 사용할 수 있을 것으로 보았다. 본론에서는 서사의 통합체/계열체적 속성과 서사가 게임에서 차지하는 비중이라는 두 축을 이용하여 각 컴퓨터게임의 장르의 위치를 설정하였다. 이에 따르면 게임장르의 서사적 특성을 반영한 위치를 찾을 수 있었다.

• 영어어문교육
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• 제15권4호
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• pp.331-344
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• 2009

The purpose of this study is to explore sorts and roles of Afro-American music genres such as jazz, blues, gospel, and swing which were shown in post-structural Afro-American literary works: Toni Morrison's novel Jazz, Alice Walker's novel The Color Purple, and August Wilson's play The Piano Lesson. It has been phenomenal for several important Afro-American writers to create their works in which they invite traditional Afro-American music genres. This has made significant effects to depict a wide range of episodes in their works, which are historically and culturally associated with such music genres. This paper analyzes varied ways in which the writers combine these two artistic fields, which are all Afro-American, and express their authenticity and identity as being blackness.

• 복식
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• 제55권8호
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• pp.1-14
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• 2005

This study aims to find out how the deconstruction and combination of genres is being carried out in modern art and design, to look at the role of art and fashion in modern culture through a study of genre hybrid tendencies in fashion, and to reposition fashion as a tool that sensitively reflects changes in society and culture. The heightening of deconstructionism, gadgets, high technology which provide a background for the fall of all art and design styles and genres and the overlap and diffusion of different styles, is the fundamental reason behind genre hybrid. In the genre hybrids of fashion, the four characteristics of nomadism, amusement, wearability and noticeability were especially strong. As a result of studying the genre hybrids in modern fashion, I found out that the idea of wearing in fashion was made easier with the deconstruction and combination of fashion with different genres. It was also apparent that the genre hybrid phenomenon will continue in the future due to the continuous development of technology.

• 한국콘텐츠학회논문지
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• 제16권11호
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• pp.707-715
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• 2016

연기자에게 가장 중요한 역할은 관객 또는 시청자에게 생각과 감정을 전달하는 것이다. 각 장르의 연기표현방법은 확연히 다르다. 연기자가 영화, TV, 연극, 뮤지컬 등 각 장르의 연기를 넘나들기 위해서는 각각의 세분화된 특성들을 이해해야 한다. 각기 다른 연기 방법론의 특성을 이해하고 각 매체의 공간적인 상황에 적응할 수 있는 표현법을 습득하면 각각의 장르를 넘나들며 연기하는 것이 좀 더 자유로워진다. 이것을 위해서 연기자는 반드시 각 장르의 특성들을 이해하고 습득하여야 한다. 우선적으로 매체연기와 무대연기의 차이점을 이해하고 각기 다른 장르의 특성을 이해하는 것이 중요하다.

• 한국멀티미디어학회논문지
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• 제4권5호
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• pp.414-422
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• 2001

본 논문에서는 오디오 데이터의 효과적인 검색을 위하여, 시간지연신경망을 이용한 음악 장르 분류 시스템을 제안한다. 분류 대상 장르는 Blues, Country, Hard Core, Hard Rock, Jazz, R&B(Soul), Techno, Trash Metal의 8종류이다. 장르를 분류하기 위한 비교단위는 곡 중에서의 한 마디이다. 이러한 마디는 리듬의 특성을 효과적으로 반영하는 스네어 드럼 소리를 기준으로 추출한다. 분류기는 시간 지연 신경망을 이용하여 구성하며 입력은 추출된 마디에 대한 주파수 특징벡터이다. 제안한 시스템의 유효성을 검증하기 위한 실험에서, 장르별 10곡씩 총 80곡의 학습 데이터와 장르별 5곡씩 총 40곡의 테스트 데이터에 대하여 각각 92.5%와 60%의 정인식율을 보였다

• International Journal of Internet, Broadcasting and Communication
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• 제12권4호
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• pp.42-47
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• 2020

YouTube, the online video streaming platform, has become popular and influential around the globe. Due to the development of science and technology, people without expertise in filming can now easily produce their videos with unique content. Many people are more eager to become a popular YouTube creator because they can earn money by placing commercials or Products in Placement (PPL) in their video clips. However, it is yet unknown what genres of YouTube videos are popular. YouTube creators have their channels where they upload videos of a certain type of genre. This study investigates video genres of the top 250 YouTube channels in English-speaking countries (United States, Canada, United Kingdom, and Australia) using Social Blade, which is a research website. The ranking is set based on the number of times people watched a video ("Video Views"). We handsomely analyze popular genres of the channels and also the YouTube ecosystem, and it will be meaningful for today's new media era.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1609-1618
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• 2016

is a service providing the story synopses that match user's query. This paper analyzes the user log of which is the answers to the queries to find stories from database, and shows the tendency distribution of user creation. Specially, we analyze a joint distribution of the genres and actions of stories to get better understanding of the tendencies that cannot be found in the analysis of independent distribution. Furthermore, we define a correlation factor between genre and action, and investigate what combinations of the genres and actions are highly, less, and negatively correlated. Finally, we investigate how the tendencies of characters are related to genres and actions, and propose a visualization method to show the tendencies.

• Journal of Information Processing Systems
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• 제20권1호
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• pp.13-23
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• 2024

Music brings pleasure and relaxation to people. Therefore, it is necessary to classify musical genres based on scenes. Identifying favorite musical genres from massive music data is a time-consuming and laborious task. Recent studies have suggested that machine learning algorithms are effective in distinguishing between various musical genres. However, meeting the actual requirements in terms of accuracy or timeliness is challenging. In this study, a hybrid machine learning model that combines a deep residual auto-encoder (DRAE) and support vector machine (SVM) for musical genre recognition was proposed. Eight manually extracted features from the Mel-frequency cepstral coefficients (MFCC) were employed in the preprocessing stage as the hybrid music data source. During the training stage, DRAE was employed to extract feature maps, which were then used as input for the SVM classifier. The experimental results indicated that this method achieved a 91.54% F1-score and 91.58% top-1 accuracy, outperforming existing approaches. This novel approach leverages deep architecture and conventional machine learning algorithms and provides a new horizon for musical genre classification tasks.