• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.48 no.5
• /
• pp.31-36
• /
• 2011

In this paper, Mel-Frequency Cepstral Coefficient (MFCC), Decorrelated Filter Bank (DFB), Octave-based Spectral Contrast (OSC), Zero-Crossing Rate (ZCR), and Spectral Contract/Roll-Off are combined as a set of multiple feature-vectors for the music genre classification system based on the Support Vector Machine (SVM) classifier. In the conventional system, feature vectors for the entire genre classes are normalized for the SVM model training and classification. However, in this paper, selected feature vectors that are compared based on the One-Against-One (OAO) SVM classifier are only used for normalization. Using OSC as a single feature-vector and the multiple feature-vectors, we obtain the genre classification rates of 60.8% and 77.4%, respectively, with the conventional normalization method. Using the proposed normalization method, we obtain the increased classification rates by 8.2% and 3.3% for OSC and the multiple feature-vectors, respectively.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.27 no.8A
• /
• pp.836-845
• /
• 2002

Video summary is one of the tools which can provide the fast and effective browsing for a lengthy video. Video summary consists of many key-frames that could be defined differently depending on the video genre it belongs to. Consequently, the video summary constructed by the uniform manner might lead into inadequate result. Therefore, identifying the video genre is the important first step in generating the meaningful video summary. We propose a new method that can classify the genre of the video data in MPEC compressed bit-stream domain. Since the proposed method operates directly on the compressed bit-stream without decoding the frame, it has merits such as simple calculation and short processing time. In the proposed method, only the visual information is utilized through the spatial-temporal analysis to classify the video genre. Experiments are done for 6 genres of video: Cartoon, commercial, Music Video, News, Sports, and Talk Show. Experimental result shows more than 90% of accuracy in genre classification for the well -structured video data such as Talk Show and Sports.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.49-55
• /
• 2019

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character's personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

• Journal of the Korean Society for information Management
• /
• v.31 no.3
• /
• pp.313-329
• /
• 2014

This study aims to provide intellectual access to TV content using faceted classification. In order to describe the content of news and current affairs programs, a faceted approach was explored. Based on the Ranganathan's PMEST formula, the basic facets - 'who', 'what', 'how', 'where', 'when' - and their sub-facets were created, specifically for describing the news genre. Additionally, the formal structure and the contextual features of the news genre were mainly considered for creating sub-facets. These created facets were applied to a news genre program. The result shows that these suggested facets are useful for representing well the contextual components of the news genre. The application of faceted classification is expected to improve the identification of the specific TV content.

• The Journal of the Korea Contents Association
• /
• v.6 no.10
• /
• pp.41-52
• /
• 2006

Animation, presenting laughing, tears and inspiration to the audience, can be classified into several genres according to the structure of scenario and storytelling in the same ways as for movie. This classification of scenario genre and scenario writing is based on the writing for a play. Scenario writing and genre classification that were discussed mainly in the movie theory books has been applied to the animation in spite of the difference of the production technique and storylines. Animation finds its academic values as a movie art due to its frame-by-frame filming technique and the diversity in the production. Since the number of scenario writers for animation is much smaller than that of movie in Korea, it is needed to set an academic basis for the classification of genre and scenario writing. In this study, academic basis for the classification of the genre by the analyzing the animation works.

• Journal of Information Technology Services
• /
• v.16 no.3
• /
• pp.167-183
• /
• 2017

The rapid growth of information technology and mobile service platforms, i.e., internet, google, and facebook, etc. has led the abundance of data. Due to this environment, the world is now facing a revolution in the process that data is searched, collected, stored, and shared. Abundance of data gives us several opportunities to knowledge discovery and data mining techniques. In recent years, data mining methods as a solution to discovery and extraction of available knowledge in database has been more popular in e-commerce service fields such as, in particular, movie recommendation. However, most of the classification approaches for predicting the movie popularity have used only several types of information of the movie such as actor, director, rating score, language and countries etc. In this study, we propose a classification-based support vector machine (SVM) model for predicting the movie popularity based on movie's genre data and social network data. Social network analysis (SNA) is used for improving the classification accuracy. This study builds the movies' network (one mode network) based on initial data which is a two mode network as user-to-movie network. For the proposed method we computed degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality as centrality measures in movie's network. Those four centrality values and movies' genre data were used to classify the movie popularity in this study. The logistic regression, neural network, $na{\ddot{i}}ve$ Bayes classifier, and decision tree as benchmarking models for movie popularity classification were also used for comparison with the performance of our proposed model. To assess the classifier's performance accuracy this study used MovieLens data as an open database. Our empirical results indicate that our proposed model with movie's genre and centrality data has by approximately 0% higher accuracy than other classification models with only movie's genre data. The implications of our results show that our proposed model can be used for improving movie popularity classification accuracy.

• Journal of Internet Computing and Services
• /
• v.25 no.4
• /
• pp.57-64
• /
• 2024

This paper aims to enhance the accuracy of music genre classification for music tracks where genre information is not provided, by utilizing machine learning to classify a large amount of music data. The paper proposes collecting and preprocessing data instead of using the commonly employed GTZAN dataset in previous research for genre classification in music. To create a dataset with superior classification performance compared to the GTZAN dataset, we extract specific segments with the highest energy level of the onset. We utilize 57 features as the main characteristics of the music data used for training, including Mel Frequency Cepstral Coefficients (MFCC). We achieved a training accuracy of 85% and a testing accuracy of 71% using the Support Vector Machine (SVM) model to classify into Classical, Jazz, Country, Disco, Soul, Rock, Metal, and Hiphop genres based on preprocessed data.

• Journal of Digital Convergence
• /
• v.17 no.8
• /
• pp.401-406
• /
• 2019

Recently, as multi-platform game environments become common, many games of convergence genre have been produced, and the boundaries of genre division by existing platforms have become blurred. The game genre is convergence content consisting of user-centered 'narrative and play'. In this paper, we propose a game genre classification according to the user 's behavior type based on the essential recognition that the subject of the game is the user. The user's actions are done in different genres and goals and rules, and the interaction is an important act for immersion. Therefore, the user's behavioral classification and perception by the game genre are important and expected to help redefine the game structure.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.31-41
• /
• 2018

Text classification has been studied for a long time in the Natural Language Processing field. In this paper, we propose an article- and paragraph-level genre classification system using Word2Vec-based LSTM, GRU, and CNN models for large-scale English corpora. Both article- and paragraph-level classification performed best in accuracy with LSTM, which was followed by GRU and CNN in accuracy performance. Thus, it is to be confirmed that in evaluating the classification performance of LSTM, GRU, and CNN, the word sequential information for articles is better than the word feature extraction for paragraphs when the pre-trained Word2Vec-based word embeddings are used in both deep learning-based article- and paragraph-level classification tasks.

• Journal of Korea Multimedia Society
• /
• v.10 no.7
• /
• pp.817-826
• /
• 2007

The existing a music genre classification research used signal feature of the part which gets 20 seconds interval of the random or the $40%{\sim}45%$ after in the music. This paper propose it to increase the accuracy of existing research to classify music genre using climax part in the music. Generally the music is divided to three parts; introduction, progress and climax. And the climax is the part which the music emphasizes and expresses the feature of the music best. So, we can get efficient result if the climax is used, when the music classify. We can get the climax in the music finding the tempo and node which uses FFT and the maximum waveform from each node. In this paper, we did a genre classification experiment which uses existing research method and proposing method. The existing method expressed 47% accuracy. And proposing method expressed 56% accuracy which is improved than existing method.