音乐情感认知模型与交互技术研究
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摘要
音乐与人类情感的运动形态之间存在着同质同构的对应关系，情感是音乐的本质性特征。音乐情感认知模型与交互技术研究是自然和谐人机交互、多媒体技术和计算机音乐研究的重要组成部分，属于人工情感的研究范畴，对于提升数字媒体和数字娱乐产品的情感交互能力、推进情感化人机交互的研究工作具有重要意义。
在理论方面，本论文构建了一个音乐领域的人工情感研究体系：从音乐情感心理模式的分析出发研究音乐情感的数学表示模型，基于音乐情感认知实验采用数据驱动的建模方法构建音乐情感认知模型；在情感认知基础上，基于情感语义的音乐检索、情感驱动的音乐合成等研究内容则构成为音乐情感交互技术的主体框架。在实践方面，音乐情感识别、音乐检索和音乐合成技术也是数字媒体和数字娱乐产业的重要支持技术，可有效应用于动漫制作、游戏开发、新媒体艺术作品创作以及非物质文化遗产的数字化保护工程中。
本论文是一项计算机应用、音乐美学、人工智能、认知心理学等多学科交叉的研究工作，在音乐情感表示模型、音乐情感认知、音乐情感表达以及编钟乐舞的数字化保护工程等方面深入展开，包括以下主要内容：
1．在音乐学相关研究基础上，以模糊语义相似关系而不是隶属度函数作为最基本的出发点，提出以语言值计算模型对环序结构的音乐情感进行建模，建立音乐情感语言值系统的语法与推理机制，并通过语义认知实验获得音乐情感空间和情感相似矩阵的具体形式；这种模型符合音乐情感的心理模式，具有更广泛的适用性；
2．对基本特征易于解析的MIDI音乐文件，提出一系列高层特征的识别算法：基于音程统计法和改进BP神经网络定位主音轨，基于音调无关编码方式和字符比对的主题旋律提取算法，基于曲式分析理论的乐段分割算法；
3．在音乐心理学和音乐理论指导下设计音乐情感认知实验，通过基于动态变异算子的基因表达式程序设计算法，构建符合音乐情感认知行为模式
There are homogeneity and isomorphism between music and emotion of human being, and emotion is the essence of music. Research on music's affective computing model plays an important role in digital entertainment and harmonious human-machine interactive, and belongs to the field of artificial emotion, which can improve the affective interactive ability of products in digital entertainment and enrich the content of human-machine interactive.
Theoretically, this thesis attempt to construct an architecture of music's affective computing, which includes such aspects as digital denotation and linguistic computing model, music emotion recognition, music retrieval based on affective semanteme, and emotion-driven algorithm composition. Technically, it can construct an effective engine for automatically incidental music for character animation constrained by some affective request, which can be used for animation, game, new media and intangible cultural heritage protection engineering.
This thesis is an intersection study of computer application, music aesthetics, artificial intelligence and cognitive psychology. The following works have been researched on music's affective computing model, music emotion recognition, and music emotion expression. We have six primary part of contributes as follows:
The 2~(nd) chapter: On the fundamental of music aesthetics and music psychics, a novel music affective model of linguistic computing is proposed. Based on semantic similarity relation among linguistic labels, this thesis models on music emotion and establishes the syntax and reasoning rules of music affective linguistic labels system. And lastly, according to a semantic recognition experiment, we construct the music affective space based on basic linguistic labels set and fuzzy relation matrix.
The 3~(rd) chapter: Aimed at MIDI files whose characters are easy parsed, a series of features recognition algorithms are proposed: key melody track based on interval statistical comparison and improved BP, key melody extraction algorithm employed
在理论方面，本论文构建了一个音乐领域的人工情感研究体系：从音乐情感心理模式的分析出发研究音乐情感的数学表示模型，基于音乐情感认知实验采用数据驱动的建模方法构建音乐情感认知模型；在情感认知基础上，基于情感语义的音乐检索、情感驱动的音乐合成等研究内容则构成为音乐情感交互技术的主体框架。在实践方面，音乐情感识别、音乐检索和音乐合成技术也是数字媒体和数字娱乐产业的重要支持技术，可有效应用于动漫制作、游戏开发、新媒体艺术作品创作以及非物质文化遗产的数字化保护工程中。
本论文是一项计算机应用、音乐美学、人工智能、认知心理学等多学科交叉的研究工作，在音乐情感表示模型、音乐情感认知、音乐情感表达以及编钟乐舞的数字化保护工程等方面深入展开，包括以下主要内容：
1．在音乐学相关研究基础上，以模糊语义相似关系而不是隶属度函数作为最基本的出发点，提出以语言值计算模型对环序结构的音乐情感进行建模，建立音乐情感语言值系统的语法与推理机制，并通过语义认知实验获得音乐情感空间和情感相似矩阵的具体形式；这种模型符合音乐情感的心理模式，具有更广泛的适用性；
2．对基本特征易于解析的MIDI音乐文件，提出一系列高层特征的识别算法：基于音程统计法和改进BP神经网络定位主音轨，基于音调无关编码方式和字符比对的主题旋律提取算法，基于曲式分析理论的乐段分割算法；
3．在音乐心理学和音乐理论指导下设计音乐情感认知实验，通过基于动态变异算子的基因表达式程序设计算法，构建符合音乐情感认知行为模式
There are homogeneity and isomorphism between music and emotion of human being, and emotion is the essence of music. Research on music's affective computing model plays an important role in digital entertainment and harmonious human-machine interactive, and belongs to the field of artificial emotion, which can improve the affective interactive ability of products in digital entertainment and enrich the content of human-machine interactive.
Theoretically, this thesis attempt to construct an architecture of music's affective computing, which includes such aspects as digital denotation and linguistic computing model, music emotion recognition, music retrieval based on affective semanteme, and emotion-driven algorithm composition. Technically, it can construct an effective engine for automatically incidental music for character animation constrained by some affective request, which can be used for animation, game, new media and intangible cultural heritage protection engineering.
This thesis is an intersection study of computer application, music aesthetics, artificial intelligence and cognitive psychology. The following works have been researched on music's affective computing model, music emotion recognition, and music emotion expression. We have six primary part of contributes as follows:
The 2~(nd) chapter: On the fundamental of music aesthetics and music psychics, a novel music affective model of linguistic computing is proposed. Based on semantic similarity relation among linguistic labels, this thesis models on music emotion and establishes the syntax and reasoning rules of music affective linguistic labels system. And lastly, according to a semantic recognition experiment, we construct the music affective space based on basic linguistic labels set and fuzzy relation matrix.
The 3~(rd) chapter: Aimed at MIDI files whose characters are easy parsed, a series of features recognition algorithms are proposed: key melody track based on interval statistical comparison and improved BP, key melody extraction algorithm employed
引文
1．马卫娟 and 方志刚，人机交互风格及其发展趋势．航空计算技术 1999．29(3)．
2．赵京封，计算机音乐与多媒体技术的应用．海南大学学报人文社会科学版，2002．20(3)：p．106-110,118．
3．朱春铃，论音乐与情感的关系．西南师范大学学报：人文社会科学版 2003．29(3)：p．174-176．
4．苏珊·朗格著(刘大基等译)，情感与形式．1986，北京：中国社会科学出版社．
5．周海宏．音乐与其表现的世界——对音乐音响与其表现对象之间关系的心理学与美学研究in音乐学研究所．1999，中央音乐学院：北京．
6．王新，论音乐与情感的关系．沈阳师范大学学报：社会科学版 2006．30(1)：p．157-158．
7．滕守尧译)，阿．鲁．，艺术与视知觉．1984，北京：中国社会科学出版社．
8．罗小平 and 黄虹，音乐心理学 1989，海口：三环出版社．
9．罗小平 and 黄虹，最新音乐心理学荟萃．1995，北京：中国文联出版公司．
10．周昌乐，心脑计算举要．2003，北京：清华大学出版社．
11. Wilson, I. Simulating artificial emotion and personality. 2004. Stanford, CA, United States: American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States.
12．原田昭．感性工学的架构—感性工学的研究领域与对象．in 中日设计教育研讨会论文集．1998．
13. Nagasawa, S. Present state of Kansei engineering in Japan. 2004. The Hague, Netherlands: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
14. Picard, R.W., Affective computing: Challenges. International Journal of Human Computer Studies, 2003.59(1-2): p. 55-64.
15．刘涛，孙守迁，and 潘云鹤，面向艺术与设计的虚拟人技术研究．计算机辅助设计与图形学学报，2004．16(11)：p．1475-1484．
16．王上飞 and 王煦法，基于“维量”思想的人工情感模型．中国科学技术大学学报 2004．34(1)：p．83-91．
17．夏慧煜，卢．，李衍达，路海明，基于控制原理和情感计算的信息推荐．自动化学报，2002．28(4)：p．481-487．
18．汤永川，虚拟人的主动感知与情感计算．2005，中国博士后科学基金项目结题报告．
19．王志良，人工心理学：关于更接近人脑工作模式的科学．北京科技大学学报，2000．22(5)： p．478-481．
20．王志良，人工心理与人工情绪．2005，计算机图形学2005年学科研讨会特邀报告．杭州．
21．刘华新，陈．，毛峡 图像的和谐情感特征分析．数据采集与处理，2001．16(1)：p．174-178．
22．毛峡，情感信息处理．遥测遥控 2000．21(6)：p．58-61．
23．吴镇扬，et al.,语音信号中的情感识别研究．软件学报，2001．12(7)：p．1050-1055．
24. ACII, http://www.affectivecomputing.org/.
25. Inokuchi, S. From knowledge engineering to Kansei engineering - a study on music performance. 1995. Tokyo, Jpn.
26. Woo, W., J.-I. Park, and Y. Iwadate. Emotion Analysis from Dance Performance Using Time-delay Neural Networks. 2000. Atlantic City, NJ, United States: Duke??University/Association for Intelligent Machinery, Durham, NC 27708-0291, United States.
27. Camurri, A., I. Lagerlof, and G Volpe, Recognizing emotion from dance movement: Comparison of spectator recognition and automated techniques. International Journal of Human Computer Studies, 2003. 59(1-2): p. 213-225.
28. Camurri, A., et al., Multimodal analysis of expressive gesture in music and dance performances. Gesture-Based Communication in Human-Computer Interaction, 2003. 2915: p. 20-39.
29. Zimmerman, J. Exploring the Role of Emotion in the Interaction Design of Digital Music Players. 2003. Pittsburgh, PA, United States: Association for Computing Machinery, New York, NY 10036-5701, United States.
30. Wang, M., N. Zhang, and H. Zhu. User-adaptive music emotion recognition. 2004. Beijing, China: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
31. Kim, S. and E. Andre. Composing affective music with a generate and sense approach. 2004. Miami Beach, FL, United States: American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States.
32. Mao, X., et al., Study on the affective property of music. Chaos, Solitons and Fractals, 2005. 26(3): p. 685-694.
33. Hevner, K., Expression in music: a discussion of experimental studies and theories. Psychological Review, 1935. 42: p. 186-204.
34. Hevner, K., Experimental studies of the elements of expression in music. American Journal of Psychology, 1936. 48: p. 246-268.
35. R.Thayer, The biopsychology of mood and arousal. 1989 Oxford University Press.
36. Katayose, H., M. Imai, and S. Inokuchi. Sentiment extraction in music. 1988. Rome, Italy: Publ by IEEE, Piscataway, NJ, USA.
37. Katayose, H., et al. Expression extraction in virtuoso music performances. 1990. Atlantic City, NJ, USA: Publ by IEEE, Piscataway, NJ, USA.
38. Liu, T., S. Sun, and Y. Pan. Emotional recognition for chime bell music. 2004. The Hague, Netherlands: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
39. Liu, T., et al. Music's Affective Computing Model based on Fuzzy logic, in WCICA2006. 2006. Dalian.
40. Liu, D., N. Zhang, and H. Zhu, Form and mood recognition of Johann Strauss's waltz centos. Chinese Journal of Electronics, 2003.12(4): p. 587-593.
41. Liu, D., N. Zhang, and H. Zhu, CAD system of music animation based on form and mood recognition. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2003.16(3): p. 283.
42. Liu, D., N. Zhang, and H. Zhu. Automatic Mood Detection from Acoustic Music Data, in Proceedings of 4rd International Conference on Music Information Retrieval, 1SMIR 2003. 2003.
43. Li, T. and M. Ogihara. Content-based music similarity search and emotion detection. 2004. Montreal, Que, Canada: Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ 08855-1331, United States.
44. Feng, Y.Z., Y.T. Zhuang, and Y.H. Pan, Query similar music by correlation degree. Advances??in Mutlimedia Information Processing - Pcm 2001, Proceedings, 2001. 2195: p. 885-890.
45．陈若涵，et al．以音乐内容为基础的情绪分析与辨识．in 2006 International Workshop on Computer Music and Audio Technology．2006．台湾，中国．
46. Pickens, J., A survey of feature selection techniques for musicinformation retrieval. 2001, Center for Intelligent Information Retrieval, University of Massachusetts, Amherst.
47. Downie, S.J., Music information retrieval. Annual Review of Information Science and Technology, 2003(37): p. 295-340.
48．王清亮，常青，and 薛向阳，音频信息检索综述．计算机科学，2004．31(6)：p．59-63．
49. Ghias, A., et al. Query by humming: Musical information retrieval in an audio database. 1995.San Francisco, CA, USA: ACM, New York, NY, USA.
50. Pampalk, E., Islands of music analysis, organization, and visualization of music archives. OGAI Journal (Oesterreichische Gesellschaft fuer Artificial Intelligence), 2003. 22(4): p. 20-23.
51. Cho, S.-B., Emotional image and musical information retrieval with interactive genetic algorithm. Proceedings of the IEEE, 2004. 92(4): p. 702-711.
52．余立功，计算机音乐——音乐节奏自动生成，in 计算机应用技术．2005，浙江大学：杭州．
53．耿瑾，et al．，自然交互研究—笔式简谱编辑器的设计与实现．计算机工程与应用，2004．40(25)：p．100-103,110．
54．冯寅 and 周昌乐，算法作曲的研究进展．软件学报，2006．17(2)：p．209-215．
55. Theodore, M., David Cope: The algorithmic composer. Computer Music Journal, 2001. 25(2): p. 70.
56. Ebcioglu, K., EXPERT SYSTEM FOR HARMONIZING FOUR-PART CHORALES. Computer Music Journal, 1988.12(3): p. 43-51.
57. Leman, M., Artificial neural networks in music research, in Computer Representations and Models in Music, A. Marsden and A. Pople, Editors. 1992, Academic Press: London, p. 265-301.
58. Toiviainen, P., Symbolic AI versus connectionism in music research, in Readings in Music and Artificiallntelligence, E. Miranda, Editor. 2000, Harwood Academic Publishers: Amsterdam, p. 47-67.
59. Eck, D. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks, in Neural Networks for Signal Processing Ⅻ, Proc. of the 2002 IEEE Workshop. 2002. New York: IEEE.
60. Biles, J.A. Genjam: A genetic algorithm for generating jazz solos, in Proc. of the ICMA 1994. San Francisc.
61. Yoo, M.J., I.K. Lee, and J.J. Choi, Background music generation using music texture synthesis. Entertainment Computing - Icec 2004, 2004. 3166: p. 565-570.
62. Lee, H.C. and I.K. Lee, Automatic synchronization of background music and motion in computer animation. Computer Graphics Forum, 2005. 24(3): p. 353-362.
63. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 1. 1975. 8(3): p. 199-249.
64. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 2. 1975. 8(4): p. 301-357.
65. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 3. 1975. 9(1): p. 43-80.66. Zadeh, L.A., Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 1996. 4(2): p. 103-111.
67．蔡运桂，艺术情感学1989，海口：三环出版社．
68．薛良编，音乐知识手册(4)．1991，北京：中国文联出版社．
69. Scruton,R.,The Aesthetics of Music.1999:Oxford University Press
70．杜亚雄，中国传统乐理教程 2004，上海：上海音乐出版社．
71．周新叶，音乐情感剖析．艺术百家，2005(4)：p．91-94．
72. Osgood, C.E., Suci, G.J., Tannenbaum, P.H., The measurement of meaning. 1957, Urbana, USA University of Illinois Press.
73．凤四海 and 黄希庭，情绪形容词词义的模糊赋值．心理学报，2004．36(6)：p．704-711．
74．马谋超，心理学中的模糊集分析．1993，贵阳：贵州科技出版社．
75．王上飞，感性信息处理在图像检索中的应用研究．in 信号与信息处理 2002，中国科学技术大学 合肥．
76. Schoen, M, Gatewood, EL, The Aesthetic Attitude In Music. Psychological Monograph, 1928(39): p. 162-183.
77. Juslin, P.N., Cue Utilization in Communication of Emotion in Music Performance: Relation Performance to Perception. Journal of Experimental Psychology, 2000. 26(6): p. 1797-1813.
78. Juslin, P.N. and P. Laukka, Emotional expression in speech and music - Evidence of cross-modal similarities. Emotions inside Out, 2003.1000: p. 279-282.
79. Tang Yongchuan, Z.J., Linguistic Modeling Based on Semantic Similarity relation among linguistic labels. Fuzzy Sets and Systems, 2006: p. accepted.
80. Lawry, J., A framework for linguistic modelling. Artificial Intelligence, 2004(155): p. 1-39.
81．金毅，中国民族音乐数据库基于旋律的检索技术研究．in 情报学．2003，上海交通大学： 上海．p．48．
82. http://www.midi.org/.
83. Lie, L., Y. Hong, and Z. Hong-Jiang. A new approach to query by humming in music retrieval. in IEEE Int'l Confon Multimedia and Expo (1CME 2001). 2001. Waseda University, Tokyo, Japan.
84. Zhu, Y. and M.S. Kankanhalli. Melody alignment and similarity metric for content-based music retrieval. 2003. Santa Clara, CA, United States: The International Society for Optical Engineering.
85．冯雅中，庄越挺，and 潘云鹤，一种启发式的用哼唱检索音乐的层次化方法．计算机研究与发展，2004．41(2)：p．333-339．
86．飞思科技产品研发中心，神经网络理论与MATLAB 7 实现．2005，北京：电子工业出版社
87. Kosugi, N., Y. Nishihara, and T. Sakata. A practical query-byhumming system for a large music database, in The ACM Multimedia. 2000. Los Angeles, CA.
88. Hsu, J.L., C.C. Liu, and A.L.P. Chen, Discovering nontrivial repeating patterns in music data. IEEE Transactions on Multimedia, 2001.3(3): p. 311-325.
89. Lo, Y.-L. and W.-L. Li. Linear time for discovering non-trivial repeating patterns in music databases. 2004. Taipei, Taiwan: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
90. Tseng, Y.-H., Content-based retrieval for music collections. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 1999: p. 176-182.
91. J.L. Hsu, C.C.L., A.L.P. Chen, Efficient Repeating Pattern Finding in Music Databases. Proc.??ACM International Conference on Information and Knowledge Management, 1998: p.281-288.
92. Liu, C.-C, J.-L. Hsu, and A.L.P. Chen, Efficient theme and non-trivial repeating pattern discovering in music databases. Proceedings - International Conference on Data Engineering, 1999: p. 14-21.
93. Shih, H.-H., S.S. Narayanan, and C.C.J. Kuo. Comparison of dictionary-based approaches to automatic repeating melody extraction. 2002. San Jose, CA, United States: The International Society for Optical Engineering.
94. Yu-lung, L., Y. Ho-cheng, and F. Mei-chin. FastPET: A Fast Non-trivial Repeating Pattern Extracting Technique for Music Data, in 2001 National Computer Symposium -Multimedia,Computer Graphics, and Image Processing. Taipei.
95. Yiru Xu, Y.W., Jiang Lin, Tao Liu, . Research on Features Recognition based Approach for Chime Bell Music Partition, in CAID&CD'2005. 2005.
96. Zhu, Y. and M. Kankanhalli. Key-based melody segmentation for popular songs. 2004. Cambridge, United Kingdom: Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ 08855-1331, United States.
97．李虻，音乐作品曲式分析．2005，重庆：西南师范大学出版社
98．杨儒怀，音乐分析论文集．2000，北京：中国文联出版社．
99. Mallat, S., Singularity detection and processing with wavelets IEEE Trans 1992(IT-38): p.617-643.
100. Inclan, C, and George C. Tiao, , Use of Cumulative Sums of Squares for Retrospective Detection of Changes in Variance. Journal of the American Statistical Association, 1994. 89: p. 913-923.
101. Bos, T., and Pongsak Hoontrakul, Estimation of Mean and Variance Episodes in the Price Return of the Stock Exchange of Thailand. Financial Risk and Financial Management, 2002. 16: p. 535-554.
102. Brown, R.L., J. Durbin, and J.M. Evans, Techniques for Testing the Constancy of Regression Relationships over Time. Journal of the Royal Statistical Society, 1975. 37(2): p. 149-163.
103. Lawry., J., Modelling and Reasoning with Vague Concepts. 2006: Springer.
104．郑日昌，蔡．，周益群，心理测量学．1999，北京：人民教育出版社．
105. A Gabrielsson, E.L., The influence of musical structure on emotional expression. Music and emotion: Theory and research, ed. P.N.J.a.J.A. Sloboda. 2001, New York: Oxford University Press.
106．唐启义 and 冯明光，实用统计分析及其DPS数据处理系统．2002，北京：科学出版社．
107. Ferreira, C, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2002, Angra do Heroismo, Portugal.
108. Ferreira, C, Gene Expression Programming: A New Adaptive Algorithm For Solving Problems. Complex Systems, 2001.13 (2): p. 87-129.
109. Ferreira, C, Gene Expression Programming and the Evolution of Computer Programs, in Recent Developments in Biologically Inspired Computing, L.N.d.C.a.F.J.V. Zuben, Editor. 2004, Idea Group Publishing, p. 82-103,.
110. Ferreira, C, Automatically Defined Functions in Gene Expression Programming, in Genetic Systems Programming: Theory and Experiences, L.d.M.M. N. Nedjah, A. Abraham, Editor. 2006, Springer-Verlag. p. 21-56.111．左劼，基因表达式编程核心技术研究，in 计算机科学．2004，四川大学：成都．
112．张克俊，求解决反问题的改进的基因表达式编程研究，in 控制理论与控制工程．2006，江西理工大学．
113．王小平 and 曹立明，遗传算法-理论、应用与软件实现．2002，西安：西安交通大学出版社．
114. Herrera, R, M. Lozano, and J.L. Verdegay, Tackling Real-Coded Genetic Algorithms: Operators and tools for the Behaviour Analysis. Artificial Intelligence Review, 1998(12): p. 265-319.
115．佘春峰．et al.,浮点遗传算法的收敛性及其在模型参数提取问题中的应用．电子学报，2000．28(3)：p．134-136,133．
116．王成栋 and 张优云，基于实数编码的自适应伪并行遗传算法．西安交通大学学报，2003．37(7)：p．707-710．
117．曲宁．浅谈好莱坞动画片的音乐创作．北京电影学院团学工作通讯 2003 [cited；Available from:http://www.bfa.edu.cn/tuanwei/doc/xy/14/03.htm．
118. Thomas, R, and Ollie Johnston, Disney Animation: The Illusion of Life, ed. W. Rawls. 1981,New York: Abbeyville Press.
119. Kovar, L., M. Gleicher, and R Pighin, Motion graphs. Acm Transactions on Graphics, 2002. 21(3): p. 473-482.
120. Zhan, F.B., Three Fastest Shortest Path Algorithms on Real Road Networks. Journal of Geographic Information and Decision Analysis, 1997.1(1): p. 69～82.
121．乐阳 and 龚健雅，Dijkstra 最短路径算法的一种高效率实现，武汉测绘科技大学学报 1999．24(3)．
122. Liu, T., S. Sun, and Y. Pan. Research on Motion Editing for Chime Bell Choreography, in CA1D&CD' 2005. 2005. Hague, The Netherlands.
123．彭冬梅，潘鲁生，and 孙守迁，数字化保护——非物质文化遗产保护的新手段．美术研究 2006(1)：p．47-51．
124．王韬．短歌行民族打击乐音色库——“曾侯乙”．1999．
125．崔宪，曾侯乙编钟钟铭校释及其律学研究．1997，北京：人民音乐出版社．
126. Yang, C, S. Sun, and K. Xu. Recovery of Cultural Activity for Digital Safeguarding of Intangible Cultural Heritage, in WC1CA2006. 2006. Dalian.
127. M., M., Music, mind, and meaning. Computer Music Journal, 1981. 5(3).
128．孙守迁，刘涛，and 沈军行，运动编辑软件 Eidolon V1.0，计算机软件著作权，Editor．2004．
129．沈军行，运动编辑与合成技术研究，in 计算机应用技术．2004，浙江大学：杭州．
2．赵京封，计算机音乐与多媒体技术的应用．海南大学学报人文社会科学版，2002．20(3)：p．106-110,118．
3．朱春铃，论音乐与情感的关系．西南师范大学学报：人文社会科学版 2003．29(3)：p．174-176．
4．苏珊·朗格著(刘大基等译)，情感与形式．1986，北京：中国社会科学出版社．
5．周海宏．音乐与其表现的世界——对音乐音响与其表现对象之间关系的心理学与美学研究in音乐学研究所．1999，中央音乐学院：北京．
6．王新，论音乐与情感的关系．沈阳师范大学学报：社会科学版 2006．30(1)：p．157-158．
7．滕守尧译)，阿．鲁．，艺术与视知觉．1984，北京：中国社会科学出版社．
8．罗小平 and 黄虹，音乐心理学 1989，海口：三环出版社．
9．罗小平 and 黄虹，最新音乐心理学荟萃．1995，北京：中国文联出版公司．
10．周昌乐，心脑计算举要．2003，北京：清华大学出版社．
11. Wilson, I. Simulating artificial emotion and personality. 2004. Stanford, CA, United States: American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States.
12．原田昭．感性工学的架构—感性工学的研究领域与对象．in 中日设计教育研讨会论文集．1998．
13. Nagasawa, S. Present state of Kansei engineering in Japan. 2004. The Hague, Netherlands: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
14. Picard, R.W., Affective computing: Challenges. International Journal of Human Computer Studies, 2003.59(1-2): p. 55-64.
15．刘涛，孙守迁，and 潘云鹤，面向艺术与设计的虚拟人技术研究．计算机辅助设计与图形学学报，2004．16(11)：p．1475-1484．
16．王上飞 and 王煦法，基于“维量”思想的人工情感模型．中国科学技术大学学报 2004．34(1)：p．83-91．
17．夏慧煜，卢．，李衍达，路海明，基于控制原理和情感计算的信息推荐．自动化学报，2002．28(4)：p．481-487．
18．汤永川，虚拟人的主动感知与情感计算．2005，中国博士后科学基金项目结题报告．
19．王志良，人工心理学：关于更接近人脑工作模式的科学．北京科技大学学报，2000．22(5)： p．478-481．
20．王志良，人工心理与人工情绪．2005，计算机图形学2005年学科研讨会特邀报告．杭州．
21．刘华新，陈．，毛峡 图像的和谐情感特征分析．数据采集与处理，2001．16(1)：p．174-178．
22．毛峡，情感信息处理．遥测遥控 2000．21(6)：p．58-61．
23．吴镇扬，et al.,语音信号中的情感识别研究．软件学报，2001．12(7)：p．1050-1055．
24. ACII, http://www.affectivecomputing.org/.
25. Inokuchi, S. From knowledge engineering to Kansei engineering - a study on music performance. 1995. Tokyo, Jpn.
26. Woo, W., J.-I. Park, and Y. Iwadate. Emotion Analysis from Dance Performance Using Time-delay Neural Networks. 2000. Atlantic City, NJ, United States: Duke??University/Association for Intelligent Machinery, Durham, NC 27708-0291, United States.
27. Camurri, A., I. Lagerlof, and G Volpe, Recognizing emotion from dance movement: Comparison of spectator recognition and automated techniques. International Journal of Human Computer Studies, 2003. 59(1-2): p. 213-225.
28. Camurri, A., et al., Multimodal analysis of expressive gesture in music and dance performances. Gesture-Based Communication in Human-Computer Interaction, 2003. 2915: p. 20-39.
29. Zimmerman, J. Exploring the Role of Emotion in the Interaction Design of Digital Music Players. 2003. Pittsburgh, PA, United States: Association for Computing Machinery, New York, NY 10036-5701, United States.
30. Wang, M., N. Zhang, and H. Zhu. User-adaptive music emotion recognition. 2004. Beijing, China: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
31. Kim, S. and E. Andre. Composing affective music with a generate and sense approach. 2004. Miami Beach, FL, United States: American Association for Artificial Intelligence, Menlo Park, CA 94025-3496, United States.
32. Mao, X., et al., Study on the affective property of music. Chaos, Solitons and Fractals, 2005. 26(3): p. 685-694.
33. Hevner, K., Expression in music: a discussion of experimental studies and theories. Psychological Review, 1935. 42: p. 186-204.
34. Hevner, K., Experimental studies of the elements of expression in music. American Journal of Psychology, 1936. 48: p. 246-268.
35. R.Thayer, The biopsychology of mood and arousal. 1989 Oxford University Press.
36. Katayose, H., M. Imai, and S. Inokuchi. Sentiment extraction in music. 1988. Rome, Italy: Publ by IEEE, Piscataway, NJ, USA.
37. Katayose, H., et al. Expression extraction in virtuoso music performances. 1990. Atlantic City, NJ, USA: Publ by IEEE, Piscataway, NJ, USA.
38. Liu, T., S. Sun, and Y. Pan. Emotional recognition for chime bell music. 2004. The Hague, Netherlands: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
39. Liu, T., et al. Music's Affective Computing Model based on Fuzzy logic, in WCICA2006. 2006. Dalian.
40. Liu, D., N. Zhang, and H. Zhu, Form and mood recognition of Johann Strauss's waltz centos. Chinese Journal of Electronics, 2003.12(4): p. 587-593.
41. Liu, D., N. Zhang, and H. Zhu, CAD system of music animation based on form and mood recognition. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence, 2003.16(3): p. 283.
42. Liu, D., N. Zhang, and H. Zhu. Automatic Mood Detection from Acoustic Music Data, in Proceedings of 4rd International Conference on Music Information Retrieval, 1SMIR 2003. 2003.
43. Li, T. and M. Ogihara. Content-based music similarity search and emotion detection. 2004. Montreal, Que, Canada: Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ 08855-1331, United States.
44. Feng, Y.Z., Y.T. Zhuang, and Y.H. Pan, Query similar music by correlation degree. Advances??in Mutlimedia Information Processing - Pcm 2001, Proceedings, 2001. 2195: p. 885-890.
45．陈若涵，et al．以音乐内容为基础的情绪分析与辨识．in 2006 International Workshop on Computer Music and Audio Technology．2006．台湾，中国．
46. Pickens, J., A survey of feature selection techniques for musicinformation retrieval. 2001, Center for Intelligent Information Retrieval, University of Massachusetts, Amherst.
47. Downie, S.J., Music information retrieval. Annual Review of Information Science and Technology, 2003(37): p. 295-340.
48．王清亮，常青，and 薛向阳，音频信息检索综述．计算机科学，2004．31(6)：p．59-63．
49. Ghias, A., et al. Query by humming: Musical information retrieval in an audio database. 1995.San Francisco, CA, USA: ACM, New York, NY, USA.
50. Pampalk, E., Islands of music analysis, organization, and visualization of music archives. OGAI Journal (Oesterreichische Gesellschaft fuer Artificial Intelligence), 2003. 22(4): p. 20-23.
51. Cho, S.-B., Emotional image and musical information retrieval with interactive genetic algorithm. Proceedings of the IEEE, 2004. 92(4): p. 702-711.
52．余立功，计算机音乐——音乐节奏自动生成，in 计算机应用技术．2005，浙江大学：杭州．
53．耿瑾，et al．，自然交互研究—笔式简谱编辑器的设计与实现．计算机工程与应用，2004．40(25)：p．100-103,110．
54．冯寅 and 周昌乐，算法作曲的研究进展．软件学报，2006．17(2)：p．209-215．
55. Theodore, M., David Cope: The algorithmic composer. Computer Music Journal, 2001. 25(2): p. 70.
56. Ebcioglu, K., EXPERT SYSTEM FOR HARMONIZING FOUR-PART CHORALES. Computer Music Journal, 1988.12(3): p. 43-51.
57. Leman, M., Artificial neural networks in music research, in Computer Representations and Models in Music, A. Marsden and A. Pople, Editors. 1992, Academic Press: London, p. 265-301.
58. Toiviainen, P., Symbolic AI versus connectionism in music research, in Readings in Music and Artificiallntelligence, E. Miranda, Editor. 2000, Harwood Academic Publishers: Amsterdam, p. 47-67.
59. Eck, D. Finding temporal structure in music: Blues improvisation with LSTM recurrent networks, in Neural Networks for Signal Processing Ⅻ, Proc. of the 2002 IEEE Workshop. 2002. New York: IEEE.
60. Biles, J.A. Genjam: A genetic algorithm for generating jazz solos, in Proc. of the ICMA 1994. San Francisc.
61. Yoo, M.J., I.K. Lee, and J.J. Choi, Background music generation using music texture synthesis. Entertainment Computing - Icec 2004, 2004. 3166: p. 565-570.
62. Lee, H.C. and I.K. Lee, Automatic synchronization of background music and motion in computer animation. Computer Graphics Forum, 2005. 24(3): p. 353-362.
63. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 1. 1975. 8(3): p. 199-249.
64. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 2. 1975. 8(4): p. 301-357.
65. Zadeh, L.A., CONCEPT OF A LINGUISTIC VARIABLE AND ITS APPLICATION TO APPROXIMATE REASONING EM DASH 3. 1975. 9(1): p. 43-80.66. Zadeh, L.A., Fuzzy logic = computing with words. IEEE Transactions on Fuzzy Systems, 1996. 4(2): p. 103-111.
67．蔡运桂，艺术情感学1989，海口：三环出版社．
68．薛良编，音乐知识手册(4)．1991，北京：中国文联出版社．
69. Scruton,R.,The Aesthetics of Music.1999:Oxford University Press
70．杜亚雄，中国传统乐理教程 2004，上海：上海音乐出版社．
71．周新叶，音乐情感剖析．艺术百家，2005(4)：p．91-94．
72. Osgood, C.E., Suci, G.J., Tannenbaum, P.H., The measurement of meaning. 1957, Urbana, USA University of Illinois Press.
73．凤四海 and 黄希庭，情绪形容词词义的模糊赋值．心理学报，2004．36(6)：p．704-711．
74．马谋超，心理学中的模糊集分析．1993，贵阳：贵州科技出版社．
75．王上飞，感性信息处理在图像检索中的应用研究．in 信号与信息处理 2002，中国科学技术大学 合肥．
76. Schoen, M, Gatewood, EL, The Aesthetic Attitude In Music. Psychological Monograph, 1928(39): p. 162-183.
77. Juslin, P.N., Cue Utilization in Communication of Emotion in Music Performance: Relation Performance to Perception. Journal of Experimental Psychology, 2000. 26(6): p. 1797-1813.
78. Juslin, P.N. and P. Laukka, Emotional expression in speech and music - Evidence of cross-modal similarities. Emotions inside Out, 2003.1000: p. 279-282.
79. Tang Yongchuan, Z.J., Linguistic Modeling Based on Semantic Similarity relation among linguistic labels. Fuzzy Sets and Systems, 2006: p. accepted.
80. Lawry, J., A framework for linguistic modelling. Artificial Intelligence, 2004(155): p. 1-39.
81．金毅，中国民族音乐数据库基于旋律的检索技术研究．in 情报学．2003，上海交通大学： 上海．p．48．
82. http://www.midi.org/.
83. Lie, L., Y. Hong, and Z. Hong-Jiang. A new approach to query by humming in music retrieval. in IEEE Int'l Confon Multimedia and Expo (1CME 2001). 2001. Waseda University, Tokyo, Japan.
84. Zhu, Y. and M.S. Kankanhalli. Melody alignment and similarity metric for content-based music retrieval. 2003. Santa Clara, CA, United States: The International Society for Optical Engineering.
85．冯雅中，庄越挺，and 潘云鹤，一种启发式的用哼唱检索音乐的层次化方法．计算机研究与发展，2004．41(2)：p．333-339．
86．飞思科技产品研发中心，神经网络理论与MATLAB 7 实现．2005，北京：电子工业出版社
87. Kosugi, N., Y. Nishihara, and T. Sakata. A practical query-byhumming system for a large music database, in The ACM Multimedia. 2000. Los Angeles, CA.
88. Hsu, J.L., C.C. Liu, and A.L.P. Chen, Discovering nontrivial repeating patterns in music data. IEEE Transactions on Multimedia, 2001.3(3): p. 311-325.
89. Lo, Y.-L. and W.-L. Li. Linear time for discovering non-trivial repeating patterns in music databases. 2004. Taipei, Taiwan: Institute of Electrical and Electronics Engineers Inc., New York, NY 10016-5997, United States.
90. Tseng, Y.-H., Content-based retrieval for music collections. SIGIR Forum (ACM Special Interest Group on Information Retrieval), 1999: p. 176-182.
91. J.L. Hsu, C.C.L., A.L.P. Chen, Efficient Repeating Pattern Finding in Music Databases. Proc.??ACM International Conference on Information and Knowledge Management, 1998: p.281-288.
92. Liu, C.-C, J.-L. Hsu, and A.L.P. Chen, Efficient theme and non-trivial repeating pattern discovering in music databases. Proceedings - International Conference on Data Engineering, 1999: p. 14-21.
93. Shih, H.-H., S.S. Narayanan, and C.C.J. Kuo. Comparison of dictionary-based approaches to automatic repeating melody extraction. 2002. San Jose, CA, United States: The International Society for Optical Engineering.
94. Yu-lung, L., Y. Ho-cheng, and F. Mei-chin. FastPET: A Fast Non-trivial Repeating Pattern Extracting Technique for Music Data, in 2001 National Computer Symposium -Multimedia,Computer Graphics, and Image Processing. Taipei.
95. Yiru Xu, Y.W., Jiang Lin, Tao Liu, . Research on Features Recognition based Approach for Chime Bell Music Partition, in CAID&CD'2005. 2005.
96. Zhu, Y. and M. Kankanhalli. Key-based melody segmentation for popular songs. 2004. Cambridge, United Kingdom: Institute of Electrical and Electronics Engineers Inc., Piscataway, NJ 08855-1331, United States.
97．李虻，音乐作品曲式分析．2005，重庆：西南师范大学出版社
98．杨儒怀，音乐分析论文集．2000，北京：中国文联出版社．
99. Mallat, S., Singularity detection and processing with wavelets IEEE Trans 1992(IT-38): p.617-643.
100. Inclan, C, and George C. Tiao, , Use of Cumulative Sums of Squares for Retrospective Detection of Changes in Variance. Journal of the American Statistical Association, 1994. 89: p. 913-923.
101. Bos, T., and Pongsak Hoontrakul, Estimation of Mean and Variance Episodes in the Price Return of the Stock Exchange of Thailand. Financial Risk and Financial Management, 2002. 16: p. 535-554.
102. Brown, R.L., J. Durbin, and J.M. Evans, Techniques for Testing the Constancy of Regression Relationships over Time. Journal of the Royal Statistical Society, 1975. 37(2): p. 149-163.
103. Lawry., J., Modelling and Reasoning with Vague Concepts. 2006: Springer.
104．郑日昌，蔡．，周益群，心理测量学．1999，北京：人民教育出版社．
105. A Gabrielsson, E.L., The influence of musical structure on emotional expression. Music and emotion: Theory and research, ed. P.N.J.a.J.A. Sloboda. 2001, New York: Oxford University Press.
106．唐启义 and 冯明光，实用统计分析及其DPS数据处理系统．2002，北京：科学出版社．
107. Ferreira, C, Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence. 2002, Angra do Heroismo, Portugal.
108. Ferreira, C, Gene Expression Programming: A New Adaptive Algorithm For Solving Problems. Complex Systems, 2001.13 (2): p. 87-129.
109. Ferreira, C, Gene Expression Programming and the Evolution of Computer Programs, in Recent Developments in Biologically Inspired Computing, L.N.d.C.a.F.J.V. Zuben, Editor. 2004, Idea Group Publishing, p. 82-103,.
110. Ferreira, C, Automatically Defined Functions in Gene Expression Programming, in Genetic Systems Programming: Theory and Experiences, L.d.M.M. N. Nedjah, A. Abraham, Editor. 2006, Springer-Verlag. p. 21-56.111．左劼，基因表达式编程核心技术研究，in 计算机科学．2004，四川大学：成都．
112．张克俊，求解决反问题的改进的基因表达式编程研究，in 控制理论与控制工程．2006，江西理工大学．
113．王小平 and 曹立明，遗传算法-理论、应用与软件实现．2002，西安：西安交通大学出版社．
114. Herrera, R, M. Lozano, and J.L. Verdegay, Tackling Real-Coded Genetic Algorithms: Operators and tools for the Behaviour Analysis. Artificial Intelligence Review, 1998(12): p. 265-319.
115．佘春峰．et al.,浮点遗传算法的收敛性及其在模型参数提取问题中的应用．电子学报，2000．28(3)：p．134-136,133．
116．王成栋 and 张优云，基于实数编码的自适应伪并行遗传算法．西安交通大学学报，2003．37(7)：p．707-710．
117．曲宁．浅谈好莱坞动画片的音乐创作．北京电影学院团学工作通讯 2003 [cited；Available from:http://www.bfa.edu.cn/tuanwei/doc/xy/14/03.htm．
118. Thomas, R, and Ollie Johnston, Disney Animation: The Illusion of Life, ed. W. Rawls. 1981,New York: Abbeyville Press.
119. Kovar, L., M. Gleicher, and R Pighin, Motion graphs. Acm Transactions on Graphics, 2002. 21(3): p. 473-482.
120. Zhan, F.B., Three Fastest Shortest Path Algorithms on Real Road Networks. Journal of Geographic Information and Decision Analysis, 1997.1(1): p. 69～82.
121．乐阳 and 龚健雅，Dijkstra 最短路径算法的一种高效率实现，武汉测绘科技大学学报 1999．24(3)．
122. Liu, T., S. Sun, and Y. Pan. Research on Motion Editing for Chime Bell Choreography, in CA1D&CD' 2005. 2005. Hague, The Netherlands.
123．彭冬梅，潘鲁生，and 孙守迁，数字化保护——非物质文化遗产保护的新手段．美术研究 2006(1)：p．47-51．
124．王韬．短歌行民族打击乐音色库——“曾侯乙”．1999．
125．崔宪，曾侯乙编钟钟铭校释及其律学研究．1997，北京：人民音乐出版社．
126. Yang, C, S. Sun, and K. Xu. Recovery of Cultural Activity for Digital Safeguarding of Intangible Cultural Heritage, in WC1CA2006. 2006. Dalian.
127. M., M., Music, mind, and meaning. Computer Music Journal, 1981. 5(3).
128．孙守迁，刘涛，and 沈军行，运动编辑软件 Eidolon V1.0，计算机软件著作权，Editor．2004．
129．沈军行，运动编辑与合成技术研究，in 计算机应用技术．2004，浙江大学：杭州．