• 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.

• Journal of information and communication convergence engineering
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• v.15 no.1
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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.

• Journal of Korea Game Society
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• v.6 no.2
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• pp.23-32
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• 2006

This article attempts to suggest a frame of game genres by appling the concept of narrative. The narrative is traditional and useful concept in game study, though the narrative of computer games is under discussion. In the main discourse, syntagmatic/paradigmatic character of narrative and the rate of importance of narrative in the game are used for two axis to set the location of computer game genres. As a result, I could find the location of computer game genres that reflects the characters of narrative.

• English Language & Literature Teaching
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• v.15 no.4
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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.

• Journal of the Korean Society of Costume
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• v.55 no.8 s.99
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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.

• The Journal of the Korea Contents Association
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• v.16 no.11
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• pp.707-715
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• 2016

The most important role of actors is to transmit the thoughts and feelings to the audience or viewers. The expression methods of acting of each genre are clearly different. In order for the actors to act, crossing over the boundary of each genre such as movies, TV, dramas, musicals, they should understand the characteristics of each of the subdivided genres. If they understand the characteristics of the acting method different from one another and acquire the expression method to adapt to the spatial circumstances of each of the media, it becomes freer to act, crossing over the boundary of each of the genres. For the sake of this, actors must understand and acquire the characteristics of each of the genres. First of all, it is important to understand the difference between acting in the media and acting on the stage and to understand the characteristics of each of the genres.

• Journal of Korea Multimedia Society
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• v.4 no.5
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• pp.414-422
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• 2001

This paper proposes a classifier of music genre using time delay neural network(TDNN) fur an audio data retrieval systems. The classifier considers eight kinds of genres such as Blues, Country, Hard Core, Hard Rock, Jazz, R&B(Soul), Techno and Trash Metal. The comparative unit to classify the genres is a melody between bars. The melody pattern is extracted based un snare drum sound which represents the periodicity of rhythm effectively. The classifier is constructed with the TDNN and uses fourier transformed feature vector of the melody as input pattern. We experimented the classifier on eighty training data from ten musics for each genres and forty test data from five musics for each genres, and obtained correct classification rates of 92.5% and 60%, respectively.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.4
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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.

• Journal of Korea Multimedia Society
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• v.19 no.8
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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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• v.20 no.1
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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.