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題名 中文流行音樂詞曲情意關聯分析
Conception association analysis between lyrics and music of Chinese popular music作者 林志傑
Lin, Chih Chieh貢獻者 沈錳坤<br>張寶芳
Shan, Man Kwan<br>Chang, Pao Fang
林志傑
Lin, Chih Chieh關鍵詞 詞曲情意關聯分析
音樂情意分析
歌詞情意分析
跨模態關聯探勘
以曲找詞
音樂資訊擷取
Conception association analysis between lyrics and music
Music conception analysis
Lyrics conception analysis
Cross modal association mining
Recommendation lyrics by song
Music Information Retrieval日期 2012 上傳時間 1-三月-2013 09:27:38 (UTC+8) 摘要 本篇論文旨在研究中文流行音樂歌詞與歌曲之間情意的關聯性，並設計一個能推薦出符合歌曲情意的「以曲找詞歌詞推薦系統」。流行音樂（Popular Music）在廣義上的定義為透過大眾媒體傳播、以大眾為閱聽對象的歌曲。其大眾化的特徵，使得流行音樂歌詞的主題多與日常生活息息相關且能清楚表達歌曲的情意，並以其所引起的共鳴性決定歌曲是否具出版的商業價值，人們也常常使用流行音樂歌曲來唱出屬於自己的故事、屬於自己的心聲。因此，本篇論文提出自動為流行音樂歌曲推薦符合歌曲情意的歌詞，讓舊有的歌曲搭配上新的歌詞，而當一首歌曲搭配了不同的歌詞就有了不同的故事，也帶給了原曲新的生命，達成一曲多詞的數位加值效果。由文獻及專業音樂創作者的論述中，我們可以了解流行音樂詞曲有相關的搭配關係，其中又以詞曲情意的搭配關係最為重要，因此詞曲情意之間的關聯性為本研究問題的核心所在。透過大量分析市面上的流行歌曲，我們便可以從中看出詞曲之間情意搭配的線索。我們利用 LSA（Latent Semantic Analysis）演算法萃取出歌詞的情意特徵值，並比較其與語言學領域中隱喻融合理論的相似性，而在歌曲方面萃取出音高、調性、速度、節奏、和弦及音色等與等能展現歌曲情意的相關特徵值。然後利用了 CFA（Cross-Modal Factor Analysis）演算法來建立詞曲之間情意特徵值的關聯模型，最後我們便可以利用關聯模型來建立推薦系統，如此便完成了詞曲情意關聯為基礎的以曲找詞歌詞推薦系統。實驗結果顯示，考慮詞曲情意特徵關聯所訓練出的關聯模型（CFA Feature Model）在以曲找詞推薦符合情意歌詞的前五名準確率平均達 60.1 %，前五十名也有 41.4 % 的準確率，比起僅考慮歌曲情意特徵（Audio Feature Model）以曲找詞推薦符合情意歌詞的前五名準確率 45.1% 及前五十名準確率28.6 % 準確率高，代表本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞。我們也對本研究提出的詞曲情意關聯模型進行歌詞推薦結果的案例分析，我們輸入幾首學生創作的歌曲觀察詞曲情意關聯模型歌詞推薦結果，我們發現推薦出的流行音樂歌詞與學生創作的原詞在歌詞情意上非常類似，再次顯示本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞，在詞曲創作上將能為創作者帶來靈感支援，幫助創作者詞曲創作。
Nowadays lots of people use popular music to sing out their own story, and their own aspirations. In this thesis, we propose an approach to analyze the conception association between lyrics and music of Chinese popular music. And for applications, we design a lyrics recommendation system which can automatically recommend lyrics which is suitable to accompany with query music according to the affection and conception between lyrics and music. So, the old song with new lyrics, just like the song with different stories, brings the original song with new life.There are accompany association between lyrics and music, and the affection and conception association is most important among all. Therefore, analyze the conception association between lyrics and music is our goal. To do this, we can find out the association clues between lyrics and music from analyzing lots of popular music. For lyrics, we use LSA (Latent Semantic Analysis) algorithm to extract lyrics conception features. For music, we extracted the pitch, tonality, speed, rhythm, chords features which can show the music’s conception in the music. Then we use the CFA (Cross-Modal Factor Analysis) algorithm to analyze and learn the conception association between lyrics and music and establish the conception association model . Finally, we will be able to take advantage of the conception association model to establish the lyrics recommendation system.In the experimental results, when recommend the same conception lyrics to the query music, our proposed approach (CFA Feature Model) reaches accuracy of 60.1% on average in the top 5 recommended lyrics. Compared to control group approach (Audio Feature Model) only reaches accuracy of 45.1% on average in the top 5 recommended lyrics, our approach get better accuracy. We also presented some interesting lyrics recommendation results in case study. We upload some popular music created by students, and we found out that the affection and conception of the recommended lyrics are similar to the original song lyric which is created by the students. The experimental results show that the lyrics and music conception association model we proposed in this study does recommended lyrics suitable to the query music conception.參考文獻 [1] K. Bischoff, C. S. Firan, R. Paiu, W. Nejdl, C. Laurier, and M. Sordo, “Music Mood And Theme Classifcation – A Hybrid Approach,” International Society for Music Information Retrieval Conference, 2009.[2] M. M. Bradley and P. J. Lang, “Affective Norms for English Words (ANEW),” The NIMH Center for The Study of Emotion and Attention, 1999.[3] R. Cai, C. Zhang, L. Zhang, and W. Y. Ma, “MusicSense: Contextual Music Recommendation using Emotional Allocation Modeling,” ACM International Conference on Multimedia, 2007.[4] S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman, “Indexing by Latent Semantic Analysis,” Journal of the American Society for Information Science, Vol. 41, No. 6, Pages 391-407, 1990.[5] Y. Hu, X. Chen, and D. Yang, “Lyric-based Song Emotion Detection with Affective Lexicon and Fuzzy Clustering Method,” International Society for Music Information Retrieval Conference, 2009.[6] X. Hu, J. S. Downie, C. Laurier, M. Bay, and A. F. Ehmann, “The 2007 MIREX Audio Mood Classification Task: Lessons Learned,” International Society for Music Information Retrieval, 2008.[7] K. S. Jones, “A Statistical Interpretation of Term Specificity and Its Application in Retrieval,” Journal of Documentation, Vol. 28, Pages 11-24, 1972.[8] P. Juslin and P. Luakka, “Expression, Perception, and Induction of Musical Emotions: A Review and Questionnaire Study of Everyday Listening,” Journal of New Music Research, Vol. 33, Issue 3, Pages 217-238, 2004.[9] Y. E. Kim, E. M. Schmidt, R. Migneco, B. G. Morton, P. Richardson, J. Scott, J. A. Speck, and D. Turnbull, “Music Emotion Recognition: A State of The Art Review,” International Society for Music Information Retrieval Conference, 2010.[10] W. Krzanowski, “Principles of Multivariate Analysis.,” Oxford University Press, 1988.[11] D. Li, N. Dimitrova, M. Li, and I. K. Sethi, “Muiltimedia Content Processing through Cross-Modal Association,” ACM International Conference on Multimedia, 2003.[12] A. Mehrabian and J. A. Russell, “An Approach to Environmental Psychology, ” MIT Press, 1974.[13] O. C. Meyers, “A Mood-Based Music Classification and Exploration System,” Master’s thesis, Massachusetts Institute of Technology, 2007.[14] J. A. Russell, “A Circumspect Model of Affect,” Journal of Psychology and Social Psychology, Vol. 39, No. 6, Page 1161, 1980.[15] F. Turner, G. Turner, and Mark Turner, “The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities,” New York: Basic Books, 2002.[16] L. Zhuang, Z. Ye, J. Wu, F. Zhou, and J. Shao, “Towards a New Reading Experience via Semantic Fusion of Text and Music,” ACM/IEEE Joint Conference on Digital Libraries, 2003.[17] 吳媺婉 ，《台灣國語流行歌曲的修辭藝術》，國立台北教育大學語言教育研究所碩士論文，2005。[18] 李雙澤，http://zh.wikipedia.org/wiki/%E6%9D%8E%E9%9B%99%E6%BE%A4。[19] 周世箴 ，《我們賴以生存的譬喻》，聯經出版公司，2006。[20] 林岑陵 ，《中國詩詞空間意境的概念在我音樂創作中的實踐》，國立師範大學音樂研究所博士論文，2010。[21] 姚霎珊，〈談談流行歌曲歌時的語言特色〉，《楚雄獅專學報》，1999。[22] 柳飛，〈通俗歌曲界定之管見〉，《常州工學院學報》，2006。[23] 胡逆天，〈流行歌曲的定義問題〉，《流行詞話》，第三期，2011。[24] 馬春樹，〈流行歌詞的比喻特色及其文化透視〉，《廣西大學學報》，2004。[25] 張雯禎，《台灣流行歌詞中的隱喻：以愛情為主題（1990-2008）》，國立中正大學語言學研究所碩士論文，2008。[26] 陳清橋，《情感的實踐：香港流行歌詞研究》，牛津大學出版社，1997。[27] 曾慧佳，《從流行歌曲看台灣社會》，桂冠出版社，1998。[28] 華語流行音樂，http://zh.wikipedia.org/wiki/%E8%8F%AF%E8%AA%9E%E6%B5%81%E8%A1%8C%E9%9F%B3%E6%A8%82。[29] 費良華，〈流行歌曲歌詞的語法規範問題〉，《白城師範高等專科學校學報》，2002。[30] 黃志華，《粵語歌詞》，三聯書店有限公司，2003。[31] 劉偉萍，〈通俗歌曲歌詞的修辭藝術〉，《漯河職業技術學院學報》，2003。[32] 鄭淑儀，《台灣流行音樂與大眾文化》，輔仁大學大眾傳播所碩士論文，1992。[33] 謝峰賜，《簡易詞曲創作入門》，新鳴遠出版有限公司，1993。[34] 簡妙如，《流行文化，美學，現代性：以八、九〇年代臺灣流行音樂的歷史重構為例》，國立政治大學新聞研究所博士論文，2002。[35] 蘇郁慧，〈青少年流行音樂偏好、態度與音樂環境之相關研究〉，《藝術教育研究》，2005。 描述 碩士
國立政治大學
數位內容碩士學位學程
99462010
101資料來源 http://thesis.lib.nccu.edu.tw/record/#G0099462010 資料類型 thesis dc.contributor.advisor 沈錳坤<br>張寶芳 zh_TW dc.contributor.advisor Shan, Man Kwan<br>Chang, Pao Fang en_US dc.contributor.author (作者) 林志傑 zh_TW dc.contributor.author (作者) Lin, Chih Chieh en_US dc.creator (作者) 林志傑 zh_TW dc.creator (作者) Lin, Chih Chieh en_US dc.date (日期) 2012 en_US dc.date.accessioned 1-三月-2013 09:27:38 (UTC+8) - dc.date.available 1-三月-2013 09:27:38 (UTC+8) - dc.date.issued (上傳時間) 1-三月-2013 09:27:38 (UTC+8) - dc.identifier (其他 識別碼) G0099462010 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/57079 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 數位內容碩士學位學程 zh_TW dc.description (描述) 99462010 zh_TW dc.description (描述) 101 zh_TW dc.description.abstract (摘要) 本篇論文旨在研究中文流行音樂歌詞與歌曲之間情意的關聯性，並設計一個能推薦出符合歌曲情意的「以曲找詞歌詞推薦系統」。流行音樂（Popular Music）在廣義上的定義為透過大眾媒體傳播、以大眾為閱聽對象的歌曲。其大眾化的特徵，使得流行音樂歌詞的主題多與日常生活息息相關且能清楚表達歌曲的情意，並以其所引起的共鳴性決定歌曲是否具出版的商業價值，人們也常常使用流行音樂歌曲來唱出屬於自己的故事、屬於自己的心聲。因此，本篇論文提出自動為流行音樂歌曲推薦符合歌曲情意的歌詞，讓舊有的歌曲搭配上新的歌詞，而當一首歌曲搭配了不同的歌詞就有了不同的故事，也帶給了原曲新的生命，達成一曲多詞的數位加值效果。由文獻及專業音樂創作者的論述中，我們可以了解流行音樂詞曲有相關的搭配關係，其中又以詞曲情意的搭配關係最為重要，因此詞曲情意之間的關聯性為本研究問題的核心所在。透過大量分析市面上的流行歌曲，我們便可以從中看出詞曲之間情意搭配的線索。我們利用 LSA（Latent Semantic Analysis）演算法萃取出歌詞的情意特徵值，並比較其與語言學領域中隱喻融合理論的相似性，而在歌曲方面萃取出音高、調性、速度、節奏、和弦及音色等與等能展現歌曲情意的相關特徵值。然後利用了 CFA（Cross-Modal Factor Analysis）演算法來建立詞曲之間情意特徵值的關聯模型，最後我們便可以利用關聯模型來建立推薦系統，如此便完成了詞曲情意關聯為基礎的以曲找詞歌詞推薦系統。實驗結果顯示，考慮詞曲情意特徵關聯所訓練出的關聯模型（CFA Feature Model）在以曲找詞推薦符合情意歌詞的前五名準確率平均達 60.1 %，前五十名也有 41.4 % 的準確率，比起僅考慮歌曲情意特徵（Audio Feature Model）以曲找詞推薦符合情意歌詞的前五名準確率 45.1% 及前五十名準確率28.6 % 準確率高，代表本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞。我們也對本研究提出的詞曲情意關聯模型進行歌詞推薦結果的案例分析，我們輸入幾首學生創作的歌曲觀察詞曲情意關聯模型歌詞推薦結果，我們發現推薦出的流行音樂歌詞與學生創作的原詞在歌詞情意上非常類似，再次顯示本研究所提出的詞曲情意關聯模型確實能有效推薦出符合歌曲情意的歌詞，在詞曲創作上將能為創作者帶來靈感支援，幫助創作者詞曲創作。 zh_TW dc.description.abstract (摘要) Nowadays lots of people use popular music to sing out their own story, and their own aspirations. In this thesis, we propose an approach to analyze the conception association between lyrics and music of Chinese popular music. And for applications, we design a lyrics recommendation system which can automatically recommend lyrics which is suitable to accompany with query music according to the affection and conception between lyrics and music. So, the old song with new lyrics, just like the song with different stories, brings the original song with new life.There are accompany association between lyrics and music, and the affection and conception association is most important among all. Therefore, analyze the conception association between lyrics and music is our goal. To do this, we can find out the association clues between lyrics and music from analyzing lots of popular music. For lyrics, we use LSA (Latent Semantic Analysis) algorithm to extract lyrics conception features. For music, we extracted the pitch, tonality, speed, rhythm, chords features which can show the music’s conception in the music. Then we use the CFA (Cross-Modal Factor Analysis) algorithm to analyze and learn the conception association between lyrics and music and establish the conception association model . Finally, we will be able to take advantage of the conception association model to establish the lyrics recommendation system.In the experimental results, when recommend the same conception lyrics to the query music, our proposed approach (CFA Feature Model) reaches accuracy of 60.1% on average in the top 5 recommended lyrics. Compared to control group approach (Audio Feature Model) only reaches accuracy of 45.1% on average in the top 5 recommended lyrics, our approach get better accuracy. We also presented some interesting lyrics recommendation results in case study. We upload some popular music created by students, and we found out that the affection and conception of the recommended lyrics are similar to the original song lyric which is created by the students. The experimental results show that the lyrics and music conception association model we proposed in this study does recommended lyrics suitable to the query music conception. en_US dc.description.tableofcontents 中文摘要....................................................i英文摘要...................................................ii誌謝.....................................................iii目錄.......................................................a圖目錄......................................................c表目錄......................................................e第一章 緒論.................................................11.1 研究背景與動機...........................................11.2 問題定義................................................21.3 研究方法................................................31.4 論文架構................................................4第二章 文獻探討與理論背景......................................62.1 流行音樂................................................62.1.1 台灣流行音樂的歷史......................................62.1.2 流行音樂的定義與分類....................................62.1.3 流行音樂的特性.........................................72.2 流行音樂歌詞.............................................82.2.1 流行音樂歌詞的特性......................................82.2.2 流行音樂中的詞曲搭配關係.................................92.3 流行音樂歌詞中的情緒及隱喻................................112.3.1 流行音樂歌詞中的情緒...................................112.3.2 流行音樂歌詞中的隱喻...................................132.3.3 以融合理論分析隱喻歌詞 ..................................132.4 歌詞情緒及語意辨識.......................................152.4.1 歌詞情緒辨識..........................................152.4.2 歌詞語意辨識..........................................18第三章 詞曲情意關聯分析與歌詞推薦..............................203.1 系統架構...............................................203.2 歌詞情意特徵值萃取.......................................213.2.1 歌詞斷詞.............................................213.2.2 歌詞處理：停用詞處理及同義詞處理.........................223.2.3 歌詞關鍵字特徵萃取：TF-IDF 低階歌詞特徵向量.............. 233.2.4 歌詞情意特徵萃取：LSA 高階歌詞特徵向量....................253.3 歌曲情意特徵值萃取.......................................303.3.1 音訊特徵與歌曲情意的關係................................303.3.2 歌曲情意特徵萃取.......................................313.4 詞曲情意關聯模型與歌詞推薦................................353.4.1 詞曲情意關聯模型演算法..................................353.4.2 詞曲情意關聯模型訓練階段................................383.4.3 詞曲情意關聯模型歌詞推薦階段.............................40第四章 實驗................................................424.1 實驗資料來源抓取.........................................434.2 實驗評估設計............................................444.3 實驗結果...............................................454.3.1 降維準確率實驗結果.....................................454.3.2 詞曲情意關聯模型以曲找詞實驗結果..........................454.3.3 《蝸牛》以曲找詞實驗案例分析.............................484.3.4 以曲找詞準確率最高歌曲實驗案例分析........................504.3.5以曲找詞準確率最低歌曲實驗案例分析.........................514.3.6 「中原大學畢業歌曲－下一站夢想」以曲找詞實驗案例分析.........534.3.7 「台北科技大學畢業歌曲－倒轉旅程」以曲找詞實驗案例分析........554.3.8 無人聲伴奏音樂以曲找詞實驗案例分析........................57第五章 結論及未來研究........................................59參考文獻....................................................i zh_TW dc.language.iso en_US - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0099462010 en_US dc.subject (關鍵詞) 詞曲情意關聯分析 zh_TW dc.subject (關鍵詞) 音樂情意分析 zh_TW dc.subject (關鍵詞) 歌詞情意分析 zh_TW dc.subject (關鍵詞) 跨模態關聯探勘 zh_TW dc.subject (關鍵詞) 以曲找詞 zh_TW dc.subject (關鍵詞) 音樂資訊擷取 zh_TW dc.subject (關鍵詞) Conception association analysis between lyrics and music en_US dc.subject (關鍵詞) Music conception analysis en_US dc.subject (關鍵詞) Lyrics conception analysis en_US dc.subject (關鍵詞) Cross modal association mining en_US dc.subject (關鍵詞) Recommendation lyrics by song en_US dc.subject (關鍵詞) Music Information Retrieval en_US dc.title (題名) 中文流行音樂詞曲情意關聯分析 zh_TW dc.title (題名) Conception association analysis between lyrics and music of Chinese popular music en_US dc.type (資料類型) thesis en dc.relation.reference (參考文獻) [1] K. 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