臺灣博碩士論文加值系統

隨著多媒體技術的進步，數位化的音樂已經廣為散佈，傳統的音樂研究集中在音樂的分類、推薦以及分析，現今的研究則試著要找出音樂與人類情緒反應的關係，第一種作法，找出音樂屬性與音樂情緒反應的關係，建立一個音樂情緒的反應模型，預測所有人的音樂心情反應，此種做法最主要的缺點就是沒有考慮到個人的差異性情緒反應；另一種作法，針對每個人訓練出音樂情緒反應模型，再利用這個模型來預測音樂情緒反應，此法雖然有考慮到個人間的差異性，但是主要的缺點則在於訓練模型時的時間浪費，事實上，雖然個人的反應存在差異性，但依然具有群組性的行為模式。
本論文主要改進以上缺點，提出一個個人化的音樂情緒反應預測分析系統，考慮使用者背景的差異性，來預測使用者的音樂情緒反應，其分析過程總共包括五個階段：1)資料前處理；2)使用者情緒反應群體分群；3)使用者情緒反應群體分類；4)音樂情緒預測；5)個人化音樂情緒反應規則整合。經過以上五個階段，就可以產生個人化的音樂情緒反應預測規則，並將之建於個人化音樂情緒反應預測系統內，用來預測音樂情緒。在使用的過程，只要預先知道使用者的背景相關資料，輸入某首音樂，便可依照音樂的屬性值，預測此人的音樂情緒反應。
本論文最後邀請二十四個人聆聽二十首音樂來做實驗，利用此訓練資料產生出個人化情緒反應預測規則，並邀請十個人聆聽四首音樂來做測試，測試結果可達百分之七十的準確度。

Abstract (in Chinese)………………………………………………………...... i
Abstract………………………………………………………............................ ii
Acknowledgements……………………………………………………….......... iv
Table of Contents………………………………………………………............. v
List of Tables……..………………………………………………….................. vi
List of Figures……..……………………………………………….................... vii
List of Algorithms………………………………………………………............ viii
Chapter 1 Introduction……………………………………………………. 1
Chapter 2 Related Work…………………………………………………... 4
2.1 Traditional Computer Music Researches…………………………… 4
2.2 Music Emotion Analysis……………………………………………. 5
Chapter 3 Personalized Music Emotion Prediction (P-MEP)…………... 8
3.1 Problem Definition………………………………………………….. 8
3.2 Feature Selection……………………………………………………. 9
3.3 Analysis Procedure………………………………………………….. 16
Chapter 4 Rule Generation of P-MEP…………………………………… 18
4.1 Data Preprocessing………………………………………………….. 18
4.2 User Emotion Group Clustering……………………………………. 20
4.3 User Group Classification…………………………………………... 24
4.4 Music Emotion Classification...…………………………………….. 27
4.5 Personalized Music Emotion Prediction Rules Integration………… 32
Chapter 5 System Implementation and Experiment……………………. 36
5.1 The P-MEP System…………………………………………………. 36
5.2 Experiment…………………….……………………………………. 38
Chapter 6 Concluding Remarks………………………………………….. 41
Reference……………………………………………………………………..... 42

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