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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 于天立(Tian-Li Yu) | |
| dc.contributor.author | Chung-Hsiang Hsueh | en |
| dc.contributor.author | 薛仲翔 | zh_TW |
| dc.date.accessioned | 2021-06-16T17:49:55Z | - |
| dc.date.available | 2013-02-01 | |
| dc.date.copyright | 2012-08-27 | |
| dc.date.issued | 2012 | |
| dc.date.submitted | 2012-08-13 | |
| dc.identifier.citation | [1] S. Arifin and P. Y. K. Cheung. Affective level video segmentation by utilizing the pleasure-arousal-dominance information. IEEE Transactions on Multimedia, 10(7):1325–1341, 2008.
[2] C.-H. Chen, M. Weng, S. Jeng, and Y. Chuang. Emotion-based Music Visualization Using Photos. Advances in Multimedia Modeling, pages 358–368, 2008. [3] C. Darwin. On the Origin of Species by Means of Natural Selection, or the Preser- vation of Favoured Races in the Struggle for Life. London: John Murray., 1859. [4] B. Detenber. A Bio-Informational Theory of Emotion: Motion and Image Size Effects on Viewers. Journal of Communication, 46(3):66–84, 1996. [5] L. J. Eshelman and J. D. Schaffer. Real-coded Genetic Algorithms and Interval- Schemata. In D. L. Whitley, editor, Foundation of Genetic Algorithms 2, pages 187–202, San Mateo, CA, 1993. Morgan Kaufmann. [6] J. Foote, M. Cooper, and A. Girgensohn. Creating Music Videos Using Automatic Media Analysis. In Proceedings of the tenth ACM international conference on Mul- timedia, pages 553–560. ACM, 2002. [7] D. E. Goldberg. Real-coded Genetic Algorithms , Virtual Alphabets , and Blocking 2 Past Use of Real-Coded Genes. Small, pages 1–21. [8] A. Hanjalic. Affective video content representation and modeling. IEEE Transac- tions on Multimedia, 7:143–154, 2005. 41 [9] J. H. Holland. Adaptation in Natural and Artificial Systems. University of Michigan Press, 1975. [10] Horner and Goldberg. Genetic algorithms and computer-assisted music composi- tion. In In Proceedings of the Fourth International Conference on Genetic Algo- rithms,, 1991. [11] X. Hua, L. Lu, and H. Zhang. Automatic Music Video Generation Based on Tem- poral Pattern Analysis. In Proceedings of the 12th annual ACM international con- ference on Multimedia, pages 472–475. ACM, 2004. [12] J. S. Jin. Music Video Affective Understanding Using Feature Importance Analysis Categories and Subject Descriptors. Audio, pages 213–219, 2010. [13] P.JuslinandP.Laukka.Expression,Perception,andInductionofMusicalEmotions: A Review and a Questionnaire Study of Everyday Listening. Journal of New Music Research, 33(3):217–238, Sept. 2004. [14] M. Mandel and D. P. W. Ellis. Song-Level Features And Support Vector Machines For Music Classification. In Proc Int Symp Music Info Retrieval, 2005. [15] J. Oliveira, F. Gouyon, L. Martins, and L. Reis. Ibt: A real-time tempo and beat tracking system. In Proc. Int. Conf. on Music Information Retrieval, 2010. [16] J. Russell. A circumplex model of affect. Journal of personality and social psychol- ogy, 39(6):1161, 1980. [17] K. R. Scherer. Emotion as a multicomponent process: A model and some cross- cultural data. Review of Personality & Social Psychology, 5:37–63, 1984. [18] E.Stevens,StanleySmith;Volkman;John;&Newman.Ascaleforthemeasurement of the psychological magnitude pitch. Journal of the Acoustical Society of America, pages 185–190, 1937. 42 [19] K.Sun,J.Yu,Y.Huang,andX.Hu.Animprovedvalence-arousalemotionspacefor video affective content representation and recognition. Database, pages 566–569, 2009. [20] R. E. Thayer. The Biopsychology of Mood and Arousal. Oxford University Press, 1989. [21] K. Tsoumakas and G. Kalliris. Multi-label classification of music into emotions. Conference of Music, pages 325–330, (2008). [22] G. Tzanetakis and P. Cook. Musical genre classification of audio signals. Speech and Audio Processing, IEEE transactions on, 10(5):293–302, July 2002. [23] P. Valdez and A. Mehrabian. Effects of color on emotions. Journal of Experimental Psycology, 123:394–409, 1994. [24] H. Wang and L. Cheong. Affective understanding in film. Circuits and Systems for Video Technology, IEEE Transactions on, 16(6):689–704, 2006. [25] M. Wang and N. Zhang. User-adaptive Music Emotion Recognition. International Conference on Signal, (60174015):1352–1355, 2004. [26] M. Xu, J. S. Jin, S. Luo, and L. Duan. Hierarchical Movie Affective Content Anal- ysis Based On Arousal and Valence Features. Emotion, pages 677–680, 2008. [27] Y. Yang, C. Liu, and H. Chen. Music Emotion Classification: A Fuzzy Approach. In Proceedings of the 14th annual ACM international conference on Multimedia, pages 81–84. ACM, 2006. [28] Y.-H. Yang, Y.-C. Lin, Y.-F. Su, and H. H. Chen. A Regression Approach to Music Emotion Recognition. IEEE Transactions on Audio, Speech, and Language Pro- cessing, 16(2):448–457, Feb. 2008. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64484 | - |
| dc.description.abstract | 近年來,由於高品質的相機,影片編輯軟體,以及音樂編曲軟體變 得愈來愈容易取得,製作影片從一項專業性的工作變成非常普遍的活 動。然而,處理家庭錄製的影片往往會耗上大量的時間。許多被廣泛 使用的商業軟體例如像是iMovie與Adobe Premiere等提供了對使用者非 常友善的介面,使得人們可以自行製作喜愛的影片,然而要幫影片加 上合適的音樂卻仍然不是一件容易的事,大多數的時候這項工作相當 依賴人類的感覺與情感。在這篇論文裡面,我們提出了一個依據情緒 內容來進行影音配對的系統,學習的部份則採用實數基因演算法來達 成。實驗結果顯示我們的系統能夠根據已知的配對資料去產生合適的 影音組合,因此這個系統能夠根據學習的資訊去推薦使用者合適的音 樂清單,進而達到加速影片製作流程的效果。 | zh_TW |
| dc.description.abstract | As the high quality cameras, video editing software and music composi- tion software are more available, making videos becomes popular. However, processing home videos is time-consuming. Some popular commercial soft- ware such as iMovie and Adobe Premiere provide user-friendly interface to help people make films on their own, but adding adequate music to a clip is still a non-trivial work that highly depends on human feelings and emotions. We propose an emotion-based evolutionary music video pairing system by utilizing affective information of videos and musics to remedy this problem in this thesis. Empirical results show that our system is capable of pairing videos with adequate musics and self-adapting to human preferences. It is expected to accelerate the process of producing attractive videos by reducing the efforts needed in searching appropriate musics for videos. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T17:49:55Z (GMT). No. of bitstreams: 1 ntu-101-R99921044-1.pdf: 2334400 bytes, checksum: d622b0f6af3a105641d5707ec7b446a3 (MD5) Previous issue date: 2012 | en |
| dc.description.tableofcontents | Acknowledgments 1
中文摘要 2 Abstract 3 1 Introduction 1 2 Background 3 2.1 Emotioncontentretrieval.......................... 3 2.1.1 Psychological studies on the relationship between low-level fea- turesandemotions.......................... 3 2.1.2 Machine learning in emotion content retrieval . . . . . . . . . . . 4 2.1.3 Affectivecontentretrievalinmusicandvideo . . . . . . . . . . . 5 2.1.4 An emerging field: exploring the emotional association between musicandvideo. .......................... 5 2.2 GeneticAlgorithm ............................. 5 2.2.1 SimpleGeneticAlgorithm(SGA) ................. 6 2.2.2 RealCodedGAs(rcGAs)...................... 9 2.3 EvolutionaryArt .............................. 10 2.3.1 EvolutionaryMusic......................... 10 2.3.2 EvolutionaryComputerGraphics ................. 13 3 Computational Models of Emotion 15 3.1 DiscreteModels............................... 15 3.2 ComponentialModels............................ 16 3.3 DimensionalModels ............................ 17 4 System Description 21 4.1 Overview .................................. 21 4.2 GeneticAlgorithms............................. 22 4.3 EmotionContentRetrieval ......................... 24 4.3.1 AudioFeatureExtraction...................... 24 4.3.2 VideoFeatureExtraction...................... 25 4.4 ProblemEncoding ............................. 27 4.5 Implementation ............................... 27 4.5.1 VideoandAudioProcessing.................... 28 4.5.2 RealCodedGAandRenderFunction ............... 30 5 Experiment 31 5.1 DatasetandPreprocessing ......................... 31 5.2 FeatureImportanceAnalysis........................ 32 5.3 CrossValidation............................... 34 5.4 SubjectTest................................. 35 6 Conclusion 40 Bibliography 41 | |
| dc.language.iso | en | |
| dc.subject | 基因遺傳演算法 | zh_TW |
| dc.subject | 演化藝術 | zh_TW |
| dc.subject | 多媒體系統 | zh_TW |
| dc.subject | 情緒辨認 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Machine Learning | en |
| dc.subject | Genetic Algorithms | en |
| dc.subject | Evolutionary Arts | en |
| dc.subject | Multimedia Systems | en |
| dc.subject | Emotion Recognition | en |
| dc.title | 以情緒為基礎的演化式影音配對系統 | zh_TW |
| dc.title | Emotion-based Evolutionary Music Video Pairing System | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 100-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭士康(Shyh-Kang Jeng),陳穎平(Ying-Ping Chen) | |
| dc.subject.keyword | 基因遺傳演算法,演化藝術,多媒體系統,情緒辨認,機器學習, | zh_TW |
| dc.subject.keyword | Genetic Algorithms,Evolutionary Arts,Multimedia Systems,Emotion Recognition,Machine Learning, | en |
| dc.relation.page | 43 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2012-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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