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系統識別號 U0026-0902201512114500
論文名稱(中文) 基於音樂氛圍的舞台燈光自動化調控模式
論文名稱(英文) A methodology for stage lighting control based on music emotion feeling
校院名稱 成功大學
系所名稱(中) 工業設計學系
系所名稱(英) Department of Industrial Design
學年度 103
學期 1
出版年 104
研究生(中文) 陳仕凱
研究生(英文) Shih-Kai Chen
電子信箱 yale0208@gmail.com
學號 P36011189
學位類別 碩士
語文別 英文
論文頁數 99頁
口試委員 指導教授-蕭世文
口試委員-王中行
口試委員-郭炳宏
中文關鍵字 自動化舞台燈光調控模式  音樂情感辨識  情感與燈光調控色彩  支持向量回歸(SVR)  音樂段落辨識 
英文關鍵字 automatic stage-lighting regulation  music emotion recognition  lighting color regulation based on music emotion and genre  support vector regression (SVR)  automatic music segment detection 
學科別分類
中文摘要 傳統燈光調控需要經過專業訓練的燈光人員來進行操作,多數控台工作人員在演出前花大量的時間將燈光與音樂搭配的序列先製成MIDI檔,同樣的演出時間卻需要兩到三倍的時間來進行前置,可以說是相當的費工費時,因此,一種電腦輔助自動化舞台燈光調控模式確實是眾望所歸的。
有鑑於音樂情感辨識的研究的成熟,以及類神經網路監督式學習機的廣泛發展和應用的基礎,漸漸對於音樂情感(氛圍)也能進行量化的描述及電腦模擬,為達成音樂情感辨識,以音樂特徵對映到賽耶情感平面(Thayer model)上,產生出線性量化的音樂情感描述值,本文收集來自於Musicovery網站點播率最高之2087首之歌曲20秒音樂片段擷取21種音樂特徵,利用主成分分析法(Principle component analysis;PCA)進行降維後,以支持向量機(Support vector machine;SVM)分類器進行特徵的交叉訓練以得到最準確之音樂特徵組合,利用支持向量回歸(Support vector regression;SVR)進行回歸訓練。另一方面,進行音樂情感燈光調控實驗並研究音樂情感與燈光調控色彩調控趨向,同樣利用支持向量回歸模擬以上結果,亦考慮音樂段落間情感氛圍和強度感受上的差異,依據音樂力度發展一套音樂段落辨識方法論,做為情感辨識和燈光亮度的指標,其後進一步地加入音樂風格和燈光色彩因素,建立一套符合音樂風格和情感,依據音樂段落進行舞台燈光調控的自動化系統。為驗證本文發展的自動化燈光調控模式,本研究邀請十名受測者進行驗證問卷,結果顯示本研究開發之燈光音樂自動化搭配,確實可以依據音樂風格和段落情感給予合適的燈光調控引發觀賞時更多的娛樂性。
英文摘要 Traditionally, the stage-lighting regulation requires professionally trained technicians to operate. However, the contemporary requirements of higher-quality performance, making this work needs more preparation before the performance. Technicians or club DJ spends two to three more times before the show to make the lighting control sequence MIDI file to match the music. It is really waste of time. Thus, A methodology for automatic stage-lighting regulation would be helped.
Music emotion recognition (MER) got much development these years, so as neural network algorithms. Music feeling has been able to be recognized and even been quantifiable by a supervised machine learning approach. In this paper, A variety of music signal features from 2087 song clips were captured and been selected the main features which are related to music emotion reflected to Thayer's emotion plane in order to produce a linear quantitative value describing music emotion. After that, the music emotion and color preferences of stage-lighting were studied. Using the experimental results trained a support vector regression (SVR) to construct simulations. To be more realistic, we developed an automatic music segment detected methodology based on music signal intensity to present different music strength and feeling of each segment. Furthermore, The factor of music genre has been studied, comprehensively develops an automatic stage-lighting based on feeling, genre and intensity of each segment of music.
論文目次 摘要 I
SUMMARY II
ACKNOWLEDGENTS III
TABLE OF CONTENT IV
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.1.1 Music emotion recognition 2
1.1.2 Lighting colors and emotional feeling 4
1.2 Purpose 5
1.3 Limitation 7
1.4 Research Framework 7
CHAPTER 2 LITERATURE 12
2.1 Emotion Model 12
2.2 Music Emotion Recognition 15
2.2.1 Music feature extraction 15
2.2.2 Supervised learning machine 16
2.3 Connection between lighting color and emotion 17
CHAPTER 3 THEORETICAL FRAMEWORK 20
3.1 Music Emotion Recognition 20
3.1.1 Audio analysis and processing 21
3.1.2 Music feature extraction 21
3.1.3 Feature dimensions reduction 24
3.1.4 Support vector machine (SVM) 25
3.1.5 Optimal kernel function parameter selection 28
3.1.6 Support vector regression (SVR) 29
3.2 Automatic Lighting Regulation Methodology 33
3.2.1 Automatic music segment detection 34
3.2.2 Audio peak and valley detection 37
3.2.3 Audio onset detection 38
CHAPTER 4 RESEARCH PROCEDURES 40
4.1 Music Emotion Recognition 40
4.1.1 Steps 40
4.1.2 Experiment music samples selecting 41
4.1.3 Music features extracting 42
4.1.4 Music feature dimensions reducing 43
4.1.5 Emotion related features selecting 44
4.1.6 Music emotion recognition SVR building 45
4.2 Lighting Color Regulation Experiment 47
4.2.1 Steps 47
4.2.2 Experiment music sample selecting 48
4.2.3 Experiment system 49
4.2.4 Experiment operation 54
4.2.5 Experiment result analysis 56
4.2.6 Lighting color regulation SVR building 59
4.2.7 Lighting color emotion map 63
4.3 Lighting Color Regulation With Music Genre 64
4.3.1 Steps 64
4.3.2 Experiment result analysis 65
4.3.3 Lighting color regulation adding music genre factor SVR building 68
4.3.4 Lighting color emotion map of each music genre 72
CHAPTER 5 MODE DISCUSSION 74
5.1 Automatic Lighting Regulation Program Structure 74
5.2 Automatic Lighting Regulation Program Operation 76
5.3 Music Genre Recognition 80
5.3.1 Music genre related feature selecting 81
5.3.2 Correlation testing 82
5.3.3 Similarity testing 82
5.4 Automatic Music Segment Detection And Lighting Brightness Regulation Approach 82
5.5 Music Emotion and Lighting Color Regulation 84
5.6 Lighting Regulation Simulation Video Construction 85
5.6.1 Image blending approach 86
5.7 Case Studies 88
CHAPTER 6 CONCLUSIONS AND RECOMMENDATIONS 90
REFERENCE 93
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