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研究生:徐斐
研究生(外文):Hsu, Fei
論文名稱:基於樂譜中音樂術語及其他影響因素之分析驗證音樂情緒軌跡追蹤系統
論文名稱(外文):A verification to the trajectory of music emotion tracking system by musical terms in the score
指導教授:鄭泗東
指導教授(外文):Cheng, Stone
口試委員:曾毓忠黃志方
口試委員(外文):Tseng, yu-chungHuang, chih-fang
口試日期:2019-07-31
學位類別:碩士
校院名稱:國立交通大學
系所名稱:工學院聲音與音樂創意科技碩士學位學程
學門:工程學門
學類:其他工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:81
中文關鍵詞:音樂情緒辨識高斯混合模型古典音樂分析
外文關鍵詞:Music Emotion RecognitionGaussian Mixture Model (GMM)Classical Music Analysis
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本研究使用音樂情緒軌跡追蹤程式,從不同的角度、類型,配合樂理,進而分析古典音樂在情緒上的變化,並在各類的古典曲風上做出比較。音樂情緒軌跡追蹤程式結合類別式情緒分類法及二維情緒平面座標作為情緒辨識的基礎模型,配合機器學習技術與音樂訊號的處理,建立即時性音樂情緒軌跡追蹤系統,將音訊所引發的情緒以視覺化的方式呈現在結果之中。音樂情緒軌跡追蹤系統採納四種不同的情緒作為辨識的基礎,並使用高斯混合模型(GMM)作為分類四種情緒邊界的手段。
基於”Pleasant”、”Solemn”、”Agitated”、”Exuberant”四種基礎的情緒分類,從中分析音色、音量、音樂事件密度、和聲不和諧度、調式等音樂的基礎特徵。古典音樂的作曲家在創作時,常藉由音樂術語及聲音變化量的符號來描繪曲子,而這些因素往往是影響整首歌曲情緒起伏變化的主因。至今已有諸多研究在心理、生理上,對歌曲進行情緒上的分析;本研究則使用程式將音樂情緒數據化,以樂理為輔,得到較為準確的古典音樂情緒分析, 與心理、生理之學說互相呼應。
This study uses the music emotion trajectory tracking program to analyze the emotional changes of classical music from different ways and types, and to compare them in various classical styles. The music emotion trajectory tracking program combines the category emotion classification method and the two-dimensional emotion plane coordinates as the basic model of emotional recognition. This system cooperates with the processing of machine learning technology and music signals to establish an instant music emotion trajectory tracking system, which will bring the emotions triggered by the audio to The visual approach is presented in the results. The music emotion trajectory tracking program adopts four different emotions as the basis for identification, and uses the Gaussian mixture model (GMM) as a means of classifying the four emotional boundaries. Based on the four basic emotion categories, namely, "Pleasant", "Solemn", "Agitated", and "Exuberant", the basic characteristics of music, such as tone, volume, music event density, harmony dissonance, and tonality are analyzed. The composers of classical music often use the symbols of musical terms and sound changes to describe the songs, and these factors are often the main causes of the emotional ups and downs of the whole song. So far, there have been many studies on the psychological and physiological aspects of the emotional analysis of songs; this study uses the program to analyze music emotions, supplemented by music theory, to obtain more accurate analysis of classical music emotions, which responds to the study of psychology and physiology.
目錄
摘要 i
ABSTRACT ii
致謝 iv
目錄 v
表目錄 vii
圖目錄 viii
一、 緒論 1
1.1 研究動機 1
1.2 研究背景 2
1.3 古典音樂相關資訊 3
1.4 文獻回顧 4
1.5 研究流程 8
二、音訊分析原理及方法 9
2.1 短時距傅立葉轉換Short Time Fourier Transform(STFT) 9
2.2 能量頻譜Power Spectrum 11
2.3 音調層級分析Pitch Class Profile(PCP) 11
2.4 高斯混合模型Gaussian Mixture Model(GMM) 14
2.5 音樂特徵與情緒模型之對應關係 19
三、音樂與情緒之關聯與變動 26
3.1音樂引發的情緒及聽者之心理反應: 26
3.2情緒環狀模型 29
3.3古典音樂於聽者產生之回饋 31
3.4音樂特徵與情緒間之關聯 31
四、研究方法 34
4.1系統架構 34
4.2音訊特徵萃取方法 35
4.2.1音色分析 35
4.2.2音量計算 37
4.2.3頻譜分佈 38
4.2.4調式偵測 39
4.2.5和聲不和諧度 40
4.2.6音樂事件密集度 41
4.2.7音訊特徵分析數學式及程式說明 42
4.2.8音訊特徵分析與相關論文對應 46
4.3訓練資料格式與內容 48
4.4訓練資料 48
4.5情緒計分方式 49
4.6樂譜分析 52
4.6.1影響因子混合應用: 52
4.6.2不同樂曲形式之曲子分析: 54
4.6.3影響情緒變動的其他因素 64
4.6.4模擬結果說明 67
4.7綜合結果比對 71
五、結論及未來展望 77
六、參考文獻 78
1.傅俊傑,「具時變情緒軌機介面之自動音樂情緒追蹤系統」,國立交通大學聲音與音樂創意科技碩士學位學程,碩士論文,民國九十九年

2.Feng, Y., Zhuang, Y., Pan, Y., “Music information retrieval by detecting mood via computational media aesthetics,” Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence Washington, DC, USA 2003.

3.W. Chai and B. Vercoe, “Detection of key change in classical piano music”, ISMIR, London, 2005.

4.L.Lu, D.Liu, and H.-J.Zhang, “Automatic mood detection and tracking of music audio signals,” IEEE Transactions on Audio, Speech, and Language Processing, vol 14 No 1,: pp. 5-18, January 2006.

5.K Trohidis, G Tsoumakas, G Kalliris, “Multilabel classification of music into emotion,” ISMIR, 2008.

6.George Tzanetakis, and Perry Cook, “Musical Genre Classification of Audio Signals”IEEE Transactions on, Speech, and Processing, vol 10, No. 5, July 2002.

7.M Zentner, D Grandjean, KR Scherer, “Emotions evoked by the sound of music: Characterization, classification, and measurement,” Emotion, Vol. 8, No. 4, pp. 494–521, 2008.

8.Juslin, P.N. 1997 “Emotional Communication in Music Performance: A Functionalist Perspective and Some Data.” Music Perception vol 14 No 4, pp 383–418.

9.K. Hevner. “Experimental studies of the elements of expression in music.” The American Journal of Psychology, vol 48 No2 pp:246–268, 1936.


10.Steven R. Livingstone, Emery Schubert and Janeen D. Loehr, “Emotional arousal and the automatic detection of musical phrase boundaries”, International Symposium on Performance Science ISBN 978-94-90306-01-4 , 2009

11.P. Gomez, B. Danuser, “Relationships Between Musical Sturcture and Psychophysiological Measures of Emotion”, Emotion, Vol. 7, No. 2, pp. 377-387,
2007.

12.Y. H. Yang, et al., “A Regression Approach to Music Emotion Recognition”,IEEE Transactions on Audio, Speech, and Language Processing,Vol. 16, No. 2, pp. 448-457, February , 2008.

13.J. C. Wang, et al., “Modeling the Affective Content of Music with a Gaussian Mixture Model”, IEEE Transactions on Affective Computing, Vol. 6, No. 1, pp.56-68, January-March , 2015.

14.Heidi-Maria Lehtonen, Henri Penttinen, Jukka Rauhala, and Vesa Välimäki1 “Analysis and modeling of piano sustain-pedal effects” Helsinki University of Technology, Laboratory of Acoustics and Audio Signal Processing,Espoo, Finland, 2007.

15.P. N. Juslin, J. A. Sloboda (Eds.), Handbook of Music and Emotion:Theory,Research, Applications, Oxford University Press, 2010.

16.C. Laurier, et al., “Exploring Relationships between Audio Features and Emotion in Music”, Proceedings of the 7th Triennial Conference of European Society for the Cognitive Sciences of Music (ESCOM 2009), pp. 260-264, Jyväskylä Finland,August 12-16, 2009.

17.E.Schubert,“Measurement and Time Series Analysis of Emotion in Music”,University of New South Wales, Ph. D Dissertation, 1999.



18.林金慧,在蒙德里安架構下闡釋情緒的樂理,台南科大學報第28期生活藝術類頁115-132 中華民國98年10月

19.Cook, N. D., & Fujisawa, T. X., (2006). The psychophysics of harmony
perception: harmony is a three-tone phenomenon. Empirical Musicology Review
1, 106-126.

20.林明穎,”音樂與情緒關係定位之研究”,國立臺灣師範大學教育心理與輔導學系,碩士論文,民國九十八年

21.許書綾,”舒伯特《A大調鋼琴奏鳴曲》(D.959)之樂曲分析及演奏詮釋”,國 立臺北教育大學人文藝術學院音樂學系,碩士論文,民國九十九年

22.Thayer, R.E. “The Biopsychology of Mood and Arousal,” Oxford University Press, NY, 1989.

23.C. Duxbury, M. Sandler, and M. Davies, “A hybrid approach to musical note onset detection,”. Digital Audio Effects Conf. (DAFX,’02), pp. 33–38, Hamburg, Germany, 2002.

24.Anders Friberg, Vittorio Colombo, “Generating Musical Performances with Director Musices” Computer Music Journal, vol 24 No 3, pp. 23–29, 2000.

25.Fabien Gouyon and Perfecto Herrera, “A beat induction method for musical audio signals,” in Proc. WIAMIS Special session on Audio Segmentation and Digital Music, 2003.

26.Hanna Järveläinen,Vesa Välimäki, “Audibility of initial pitch glides in string instrument sounds,” Proceedings of the International Computer Music Conference, vol 17-23 ,pp:282–285, Havana, Cuba, September 2001.

27.Andre Holzapfel and Yannis Stylianou, “Beat tracking using group delay based onset detection,” in MIREX at 7th International ISMIR 2008 Conference, 2008

28.V. Kandia and Y. Stylianou, “Detection of clicks based On group delay,”Accepted in Canadian Acoustics, 2008.

29.Simon Dixon, “Onset detection revisited,” In Proc. of the Int. Conf. on Digital Audio Effects (DAFx-06), pages 133–137, Montreal, Quebec, Canada, Sept. 18–20, 2006.

30.K Lee, “Automatic chord recognition from audio using enhanced pitch class profile”, Proceedings of the International Computer Music, 2006.

31.Dixon, S.“An interactive beat tracking and visualisation system,” International Computer Music Conference, pages 215–218, San Francisco CA, 2001.

32.Jyh-Shing Roger Jang, “Audio Signal Processing and Recognition,” available at the links for on-line courses at the author's homepage. http://www.cs.nthu.edu.tw/~jang.

33.Jyh-Shing Roger Jang, “Speech and Audio Processing Toolbox,” available from the link at the author's homepage http://www.cs.nthu.edu.tw/~jang. 2013.

34.陳若涵、許肇凌、張智星、羅鳳珠,「以音樂內容為基礎的情緒分析與辨識」,國立清華大學資訊系統與應用所,2006 International Workshop on Computer Music and Audio Technology,WOCMAT 2006.

35.Yi-Hsuan Yang, Homer H Chen, Music Emotion Recognition, CRC Press, London, 2010.
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