A Study on Kansei Engineering of Game Sound Effect and Player’s Affective

碩士 === 南台科技大學 === 多媒體與電腦娛樂科學系 === 101 ===   Digital game industry in recent years, the rapid growth of output, from the early family computer until now the next generation of games, not only gaming platform, more and more players for the game screen, shot in more and more stress. In order to grasp t...

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Main Authors: Chen,Syuan-Ru, 陳宣如
Other Authors: Lin,Pei-Ju
Format: Others
Language:zh-TW
Published: 102
Online Access:http://ndltd.ncl.edu.tw/handle/64907628915407933511
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spelling ndltd-TW-101STUT86410052015-10-13T23:10:33Z http://ndltd.ncl.edu.tw/handle/64907628915407933511 A Study on Kansei Engineering of Game Sound Effect and Player’s Affective 應用感性工學於遊戲音效與玩家情感之研究 Chen,Syuan-Ru 陳宣如 碩士 南台科技大學 多媒體與電腦娛樂科學系 101   Digital game industry in recent years, the rapid growth of output, from the early family computer until now the next generation of games, not only gaming platform, more and more players for the game screen, shot in more and more stress. In order to grasp the market as well as the player's first impression, most of the games industry are almost always focused on the center of gravity "planning", "art" and "programs" in three points. In addition to music type of game, there are few operators to focus on the "musical sound" on. Therefore, this study selected sound theme game sound and explore the emotional response relationship between players and connected, and its main purpose can be summarized as the following three points: (1)the affective gaming audio category labels; (2)game sound the emotional response categories; (3)game sound design reference.   In this study, Kansei Engineering as a starting point, the first preliminary study to identify representative game type, and then type audio for gaming and sound emotional vocabulary sample collection filter, and finally sorting through factor analysis and the use of labels to establish an emotional support vector machine classification model . The results show that emotional reactions classify the results of three sets of scales classification labels are "emotional sensitivity", "suggestive" and "variability" and category labels for these three groups, respectively, for audio analysis, we can see that each segment label and volume, timbre and tone that characterized the relationship between the three voices. Eventually, by the support vector machine have obtained the best training model average accuracy rate was 94.4%, indicating that the results of the feasibility study. And if the designer for these indicators to be refined, but will also make sound reaches the emotional needs and matching games. Lin,Pei-Ju 林佩儒 102 學位論文 ; thesis 75 zh-TW
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language zh-TW
format Others
sources NDLTD
description 碩士 === 南台科技大學 === 多媒體與電腦娛樂科學系 === 101 ===   Digital game industry in recent years, the rapid growth of output, from the early family computer until now the next generation of games, not only gaming platform, more and more players for the game screen, shot in more and more stress. In order to grasp the market as well as the player's first impression, most of the games industry are almost always focused on the center of gravity "planning", "art" and "programs" in three points. In addition to music type of game, there are few operators to focus on the "musical sound" on. Therefore, this study selected sound theme game sound and explore the emotional response relationship between players and connected, and its main purpose can be summarized as the following three points: (1)the affective gaming audio category labels; (2)game sound the emotional response categories; (3)game sound design reference.   In this study, Kansei Engineering as a starting point, the first preliminary study to identify representative game type, and then type audio for gaming and sound emotional vocabulary sample collection filter, and finally sorting through factor analysis and the use of labels to establish an emotional support vector machine classification model . The results show that emotional reactions classify the results of three sets of scales classification labels are "emotional sensitivity", "suggestive" and "variability" and category labels for these three groups, respectively, for audio analysis, we can see that each segment label and volume, timbre and tone that characterized the relationship between the three voices. Eventually, by the support vector machine have obtained the best training model average accuracy rate was 94.4%, indicating that the results of the feasibility study. And if the designer for these indicators to be refined, but will also make sound reaches the emotional needs and matching games.
author2 Lin,Pei-Ju
author_facet Lin,Pei-Ju
Chen,Syuan-Ru
陳宣如
author Chen,Syuan-Ru
陳宣如
spellingShingle Chen,Syuan-Ru
陳宣如
A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
author_sort Chen,Syuan-Ru
title A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
title_short A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
title_full A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
title_fullStr A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
title_full_unstemmed A Study on Kansei Engineering of Game Sound Effect and Player’s Affective
title_sort study on kansei engineering of game sound effect and player’s affective
publishDate 102
url http://ndltd.ncl.edu.tw/handle/64907628915407933511
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