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    Title: 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
    Authors: 林志傑
    Lin, Chih Chieh
    Contributors: 沈錳坤
    張寶芳

    Shan, Man Kwan
    Chang, Pao Fang

    林志傑
    Lin, Chih Chieh
    Keywords: 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
    Date: 2012
    Issue Date: 2013-03-01 09:27:38 (UTC+8)
    Abstract: 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.
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    Description: 碩士
    國立政治大學
    數位內容碩士學位學程
    99462010
    101
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0099462010
    Data Type: thesis
    Appears in Collections:[數位內容碩士學位學程] 學位論文
    [數位內容碩士學位學程] 學位論文

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