清代自康熙開始，由朝廷大量纂修官書，其中又以乾隆時期所編纂的《四庫全書》為集大成之作。《四庫全書》的編纂可謂中國文化史上空前的浩大工程，歷時十五年，收錄圖書三千五百零三種，總計七萬九千餘卷，三萬六千餘頁，投入此項工程的文人學士超過四千人。因應《四庫全書》編纂的需要而產生《四庫全書總目》，其編纂歷時十六年，直至乾隆五十四（西元一七八九年）年才大功告成。《四庫全書總目》乃清代總結前代的文學批評巨著，代表了清代官方的文學觀；而集部的編纂內含一套值得吾人關注的文學批評意識於其中；再者，可與唐詩並稱的詞曲在《四庫全書總目》中何以被視為小道、館臣是否認同言志的傳統、以及被編為一類的詞曲二體為何卻有輕重之分？由以上種種觀之，詞曲兩類雖被評為小道、末流，在《四庫全書》中所佔比例也最小，但實際上《四庫全書總目》〈提要〉中的詞曲類仍有許多待研究的問題，本文即試圖以集部詞曲類為範圍，對於之中含藏的文學批評觀點，以及其觀點形成的可能原因，加以探討分析。 本文共分五章，首章緒論分四節：第一節陳述問題，第二節分析文獻，第三節界定研究範圍，第四節說明研究方法。第二章分三節說明《四庫全書總目》之形成背景、編纂經過與學術價值：第一節論述《四庫全書總目》之形成背景，第二節說明《四庫全書總目》之編纂經過，第三節則陳述《四庫全書總目》之學術價值。第三章分三節說明《四庫全書總目》詞曲類〈提要〉之詞曲觀：第一節說明詞曲特質，第二節陳述詞曲流變，第三節論述詞曲價值。第四章分三節探討《四庫全書總目》詞曲類〈提要〉詞曲觀之形成：第一節探討辨體觀念，第二節探討雅俗觀念，第三節則探討通變觀念。第五章結論：綜合上述章節，總結論述成果，試圖還原館臣在編纂《四庫全書總目》時對詞曲兩類文體的價值觀。

目前大部分的聽眾主要是透過歌詞與樂曲的搭配來了解音樂所要表達的內容，因此歌詞創作在目前的音樂工業是很重要的一環。一般流行音樂創作是由作曲人與作詞人共同完成，然而有另一種方式是將既有的詩詞做為歌詞，接著重新譜曲的方式產生新的流行音樂。這種創作方式是讓舊有的詞或曲注入新的生命力，得以流傳到現在。因此本研究希望可以為一首旋律推薦適合配唱的歌詞，以對數位音樂達到舊曲新詞的加值應用。本論文包括兩個部分，分別為：(1)自動分析歌詞的詞式，找出每個段落的位置與其段落的標籤；(2)詞曲結構搭配，找出相符合結構的詞與曲，並且同步每個漢字與音符。 本論文的第一部分為詞式分析，首先將歌詞擷取四個面向的特徵值，分別為(1)句字數結構；(2)拼音結構；(3)詞性；(4)聲調音高。第二步驟，利用這四種特徵值分別建立詞行的自相似度矩陣(Self Similarity Matrix)，並且利用這四個特徵的自相似度矩陣產生一個線性組合自相似度矩陣。第三步驟，建立在自相似度矩陣上我們做段落分群以及家族(Family)組合找出最佳的分段方式，最後將找出的分段方式利用我們整理出來的規則讓電腦自動標記段落標籤。第二部分為詞曲結構搭配，首先我們將主旋律的樂句以及歌詞的詞句做第一層粗略的對應，第二步驟，將對應好的樂句與詞句做第二層漢字與音符細部的對應，最後整合兩層對應的成本當做詞曲搭配的分數。 我們以KKBOX音樂網站當做歌詞來源，並且請專家標記華語流行歌詞資料庫的詞式。實驗顯示詞式分析的Pairwise f-score準確率達到0.83，標籤回復準確率達到0.78。詞曲結構搭配中，查詢的歌曲其原本搭配的歌詞，推薦排名皆為第一名。 / Nowadays, lots of pop music audiences understand the content of music via lyrics and melody collocation. In general, a Chinese pop music is produced by composer and lyricist cooperatively. However, another producing manner is composing new melody with ancient poetry. Therefore, we want to recommend present lyrics for a melody and then achieving value-added application for digital music. This thesis includes two subjects. The first subject is lyrics form analysis. This subject is finding the block of verse, chorus, etc., in lyrics. The second subject is structure alignment between lyrics and melody. We utilize the result of lyrics form analysis and then employ a 2-tier alignment to recommend present lyrics which is suitable for singing. In lyrics form analysis, the first step, we investigate four types of feature from lyrics: (1) Word Count Structure; (2) Pinyin Structure; (3) Part of Speech Structure; (4) Word Tone Pitch. For the second step, we utilize these four types of feature to construct a SSM(Self Similarity Matrix), and blend these four types of SSM to produce a linear combination SSM. The third step is clustering blocks and finding the best Family combination based on SSM. Finally, a rule-based technique is employed to label blocks of lyrics. For the second subject, the first step is aligning music phrases and lyrics sentences roughly. The second step is aligning a word and a note for corresponding phrase and sentence. Finally, we integrated the cost of two-level alignment regarded as the lyrics and melody collocation score. We collect lyrics from KKBOX, a music web site, and invite experts label ground truth of lyrics form. The experimental result of lyrics form analysis shows that the proposed method achieves the Pairwise f-score of 0.83, and the Label Recovering Ratio of 0.78. The experiment of structure alignment between lyrics and melody shows that the original lyrics of query melodies are ranked number one.

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.