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研究生(外文):Hsu, Cheng-Kai
論文名稱(外文):Comparison of Music Emotion Classification and Interpretation System Based on Different Genre of Training Data and Applied to Soundscape Emotion Recognition and Mixing Audio Analysis
指導教授(外文):Cheng, Stone
口試委員(外文):Huang, Chih-FangJang, Wen-YeaCheng, Stone
外文關鍵詞:Music Emotion RecognitionGaussian Mixture Model (GMM)Soundscape
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This study presents an approach to analyze the inherent emotional ingredients in the polyphonic music signals, and applied to the soundscape emotion analysis. The proposed real-time music emotion trajectory tracking systems are established by maching learning techniques, music signal processing, and the integration of two-dimensional emotion plane and categorical taxonomy as emotion recognition model. Two sets of training data are collected, one is consisted of popular music and the other is consisted of western classical music, each set contains 192 emotion-predefined music clips respectively. Volume, onset density, mode, dissonance, and timbre are extracted to serve as the characteristics of a music excerpt. After emotion score counting process, Gaussian mixture model (GMM) is used to demarcate the margins between four emotion states. A graphical interface with mood locus on emotion plane is established to trace the alteration of music-evoked human emotions. Experimental result verified that different sets of training data would lead to the variation of boundaries among two emotion recognition models. Soundscape specifies the auditory environment of human daily activities, which can affect emotion states and living quality of human beings. This study proposed an access to environmental sound designing based on emotion recognition and psychoacoustic, especially focusing on the needs of various fields for commercial purpose or auditory atmosphere creation. The soundscape study is conducted by evaluating the effectiveness of emotion locus variation of selected urban soundscape sets blending with music signals. The simulation of playing background music in authentic field makes good use of music emotional characteristics to help people alter the emotion states and the state of mind, and further affect human behavior and decision-making.
1.1 研究動機-1
1.2 音樂資訊相關研究-3
1.2.1 小波轉換(Wavelet Transform)與音訊壓縮、樂器辨識-3
1.2.2 隱藏式馬可夫模型(Hidden Markov Model,HMM)與和弦辨識-5
1.2.3 聲景(Soundscape)-7
1.2.4 音樂情緒辨識相關研究-9
1.3 文獻回顧-11
1.4 研究流程-15
2.1 情緒模型-17
2.1.1 類別式-17
2.1.2 維度式-21
2.1.3 樣板式-23
2.2 音樂特徵-26
2.2.1 音色(Timbre)-26
2.2.2 音量(Volume)-27
2.2.3 速度(Tempo)-28
2.2.4 和聲(Harmony)-29
2.2.5 調性(Mode)-30
2.2.6 相關研究使用之音樂特徵值統整歸納-30
2.3 音樂特徵與情緒模型的對應關係-31
2.3.1 Patrik N. Juslin 與 Renee Timmers研究-31
2.3.2 Cyril Laurier等人研究-32
2.3.3 Emery Schubert研究-35
4.1 系統架構-48
4.2 訓練資料-49
4.3 預處理-50
4.4 音樂訊號特徵萃取演算法-50
4.4.1 音色分析(Timbre Analysis)-50
4.4.2 音量計算(Volume Calculation)-53
4.4.3 音樂事件密度(Onset Density)-55
4.4.4 和聲不和諧度(Dissonance)-58
4.4.5 調性偵測(Mode Detection)-59
4.5 情緒分數計算-62
4.6 訓練模式結果分析-65
4.7 音樂情緒辨識系統-73
4.7.1 古典音樂情緒軌跡追蹤系統展示-73
4.7.2 古典音樂情緒軌跡追蹤系統表現評估-78
4.7.3 流行音樂情緒軌跡追蹤系統展示-88
4.7.4 流行音樂情緒軌跡追蹤系統表現評估-92
4.8 人聲語音情緒分析-99
4.8.1 北韓新聞主播李春姬播音情緒分析-99
4.8.2 台灣新聞主播葉佳蓉播音情緒分析-102
4.8.3 台灣體育主播陳宏宜播音情緒分析-105
5.1 研究方法與目的-108
5.2 實驗使用設備與軟體-110
5.2.1 硬體設備-110
5.2.2 音訊編輯軟體-113
5.3 聲景音訊錄製與情緒分析-114
5.3.1 餐廳聲景情緒分析-114
5.3.2 賣場聲景情緒分析-123
附錄A –文獻[7]音樂特徵萃取明細-141
附錄B –文獻[8]音樂特徵萃取明細-142
附錄C –古典音樂訓練資料明細-143
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