English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 68575/102759 (67%)
造訪人次 : 18856769      線上人數 : 497
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/106881


    請使用永久網址來引用或連結此文件: http://nccur.lib.nccu.edu.tw/handle/140.119/106881


    題名: 基於歌詞文本分析技術探討音樂情緒辨識之方法研究
    Exploring Music Emotion Recognition via Textual Analysis on Song Lyrics
    作者: 陳禔多
    貢獻者: 蔡銘峰
    陳禔多
    關鍵詞: 音樂情緒辨識
    日期: 2017
    上傳時間: 2017-03-01 17:14:04 (UTC+8)
    摘要: 音樂是一種情感豐富的媒體。即使跨越了數個世紀,人們還是會
    對同一首歌曲的情緒表達有類似的理解。然而在現今的數位音樂資料
    庫可以看出,我們是不可能憑著人力完成數量如此龐大的音樂情緒辨
    識,也因此期待電腦可以協助完成如此繁重的工作。隨著機器學習的
    發展,電腦逐漸可以透過統計模型與數學模型判斷與辨識一些並未事
    先提供規則的資料,而無法言傳的音樂情緒也得以有機會交由電腦辨
    識、分類。雖然目前有許多透過訊號處理技術進行的音樂辨識研究,
    但是透過歌詞文本的辨識卻是相對少見,使用的特徵也多侷限於通用
    的文字資訊。本研究以音訊特徵為基礎,從不同的歌詞文本資訊出
    發,透過分析歌詞文本進行歌曲情緒辨識,提供更多優化的參考資
    訊,藉以提升歌曲於交流、表達、推薦等互動的功能性與準確性。實
    驗結果發現,歌詞文本資訊對於歌曲的正負面情緒辨識確實有相當好
    的表現,而對於特定分類的限制則是值得更多透過不同自然語言處理
    的方法強化的。
    參考文獻: [1] J. Bergstra, N. Casagrande, D. Erhan, D. Eck, and B. K´egl. Aggregate features and
    adaboost for music classification. Machine Learning, 65(2-3):473–484, 2006.
    [2] M. M. Bradley and P. J. Lang. Affective norms for english words (anew): Instruction
    manual and affective ratings. Technical report, Technical Report C-1, The Center
    for Research in Psychophysiology, University of Florida, 1999.
    [3] M. Brysbaert and B. New. Moving beyond kuˇcera and francis: A critical evaluation
    of current word frequency norms and the introduction of a new and improved word
    frequency measure for american english. Behavior Research Methods, 41(4):977–
    990, 2009.
    [4] C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM
    Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software
    available at http://www.csie.ntu.edu.tw/˜cjlin/libsvm.
    [5] C. Cortes and V. Vapnik. Support-vector networks. Machine Learning, 20(3):273–
    297, 1995.
    [6] A. Esuli and F. Sebastiani. Sentiwordnet: A publicly available lexical resource for
    opinion mining. In Proceedings of the 5th Conference on Language Resources and
    Evaluation, pages 417–422, 2006.
    [7] Y. Feng, Y. Zhuang, and Y. Pan. Popular music retrieval by detecting mood. In
    Proceedings of the 26th Annual International ACM SIGIR Conference on Research
    and Development in Informaion Retrieval, pages 375–376. ACM, 2003.
    [8] S. Hallam, I. Cross, and M. Thaut. Oxford handbook of music psychology. Oxford
    University Press, 2008.
    [9] T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of the 22nd
    Annual International ACM SIGIR Conference on Research and Development in Information
    Retrieval, pages 50–57. ACM, 1999.
    [10] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by
    combining lyrics and audio. In Proceedings of the 10th Annual Joint Conference on
    Digital Libraries, pages 159–168. ACM, 2010.
    [11] X. Hu and J. S. Downie. When lyrics outperform audio for music mood classification:
    a feature analysis. In Proceedings of International Society of Music Information
    Retrieval Conference, pages 1–6, 2010.
    [12] X. Hu, J. S. Downie, and A. F. Ehmann. Lyric text mining in music mood classification.
    American Music, 183(5,049):2–209, 2009.
    [13] Y. Hu, X. Chen, and D. Yang. Lyric-based song emotion detection with affective
    lexicon and fuzzy clustering method. In Proceedings of International Society of
    Music Information Retrieval Conference, pages 123–128, 2009.
    [14] R. Kempter, V. Sintsova, C. Musat, and P. Pu. Emotionwatch: Visualizing finegrained
    emotions in event-related tweets. In Proceedings of the 8th International
    AAAI Conference on Weblogs and Social Media, 2014.
    [15] L.-W. Ku, Y.-T. Liang, and H.-H. Chen. Opinion extraction, summarization and
    tracking in news and blog corpora. In Proceedings of AAAI spring symposium:
    Computational approaches to analyzing weblogs, pages 100–107, 2006.
    [16] C. Laurier, J. Grivolla, and P. Herrera. Multimodal music mood classification using
    audio and lyrics. In Proceedings of the 7th International Conference on Machine
    Learning and Applications, pages 688–693. IEEE, 2008.
    [17] C. Laurier and P. Herrera. Audio music mood classification using support vector
    machine.
    [18] J. H. Lee and J. S. Downie. Survey of music information needs, uses, and seeking
    behaviours: Preliminary findings. In Proceedings of the 5th International Conference
    on Music Information Retrieval, pages 441–446, 2004.
    [19] T. Li and M. Ogihara. Content-based music similarity search and emotion detection.
    In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal
    Processing, volume 5, pages V–705. IEEE, 2004.
    [20] M. I. Mandel and D. P. Ellis. Song-level features and support vector machines for
    music classification. In Proceedings of International Conference on Music Information
    Retrieval, pages 594–599, 2005.
    [21] L. Martin and P. Pu. Prediction of helpful reviews using emotions extraction. In
    Proceedings of the 28th AAAI Conference on Artificial Intelligence, 2014.
    [22] R. Mayer, R. Neumayer, and A. Rauber. Rhyme and style features for musical genre
    classification by song lyrics. 2008.
    [23] M. F. Mckinney and J. Breebaart. Features for audio and music classification. In
    Proceedings of International Conference on Music Information Retrieval, 2003.
    [24] R. Plutchik. The nature of emotions. American Scientist, 89:344, 2001.
    [25] J. F. Y. W. Robert J Ellis, Zhe Xing. Quantifying lexical novelty in song lyrics.
    In Proceedings of the 16th International Society for Music Information Retrieval
    Conference, 2015.
    [26] J. A. Russell. Affective space is bipolar. Journal of Personality and Social Psychology,
    37(3):345–356, 1979.
    [27] J. A. Russell. A circumplex model of affect. Journal of personality and social
    psychology, 39(6):1161–1178, 1980.
    [28] P. Saari and T. Eerola. Semantic computing of moods based on tags in social media
    of music. IEEE Transactions on Knowledge and Data Engineering, 26(10):2548–
    2560, 2014.
    [29] K. R. Scherer. What are emotions? and how can they be measured? Social Science
    Information, 44(4):695–729, 2005.
    [30] V. Sintsova, C.-C. Musat, and P. Pu. Fine-grained emotion recognition in olympic
    tweets based on human computation. In Proceedings of the 4thWorkshop on Computational
    Approaches to Subjectivity, Sentiment and Social Media Analysis, number
    EPFL-CONF-197185, 2013.
    [31] P. J. Stone, D. C. Dunphy, and M. S. Smith. The general inquirer: A computer
    approach to content analysis. 1966.
    [32] G. Tzanetakis. Music analysis, retrieval and synthesis of audio signals marsyas. In
    Proceedings of the 17th ACM International Conference on Multimedia, pages 931–
    932. ACM, 2009.
    [33] M. Van Zaanen and P. Kanters. Automatic mood classification using tf*idf based
    on lyrics. In Proceedings of the 11th International Society of Music Information
    Retrieval Conference, pages 75–80, 2010.
    [34] Y.-H. Yang, Y.-C. Lin, H.-T. Cheng, I.-B. Liao, Y.-C. Ho, and H. H. Chen. Toward
    multi-modal music emotion classification. In Proceedings of Pacific-Rim Conference
    on Multimedia, pages 70–79. Springer, 2008.
    [35] Y.-H. Yang and J.-Y. Liu. Quantitative study of music listening behavior in a social
    and affective context. IEEE Transactions on Multimedia, 15(6):1304–1315, 2013.
    描述: 碩士
    國立政治大學
    資訊科學學系
    101753006
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G1017530061
    資料類型: thesis
    顯示於類別:[資訊科學系] 學位論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    006101.pdf1046KbAdobe PDF0檢視/開啟


    在政大典藏中所有的資料項目都受到原著作權保護.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回饋