政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/37105

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/37105

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/37105

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/37105

Title:	流行音樂組曲之電腦音樂編曲 Computer Music Arrangement for Popular Music Medley
Authors:	董信宗 Tung,Hsing-Tsung
Contributors:	沈錳坤 Shan,Man-Kwan 董信宗 Tung,Hsing-Tsung
Keywords:	組曲編曲音樂分段線性排列模型訓練間奏產生旋律發展和弦進程節奏辨識音樂動機探勘基因演算法 Medley Arrangement Music Segmentation Linear Arrangement Model Training Bridge Generation Melody Progression Chord Progression Rhythm Recognition Music Motivic Mining Genetic Algorithm
Date:	2006
Issue Date:	2009-09-19 12:09:55 (UTC+8)
Abstract:	在音樂中，組曲是一種特別的創作形式。組曲將多首音樂段落組合排列，並且在音樂段落之間加入間奏，形成一首音樂組曲。組曲的編曲重點在於音樂段落的編排順序及段落之間的連結。平時在宴會、舞會、餐廳、賣場等場合中，往往都會連續播放多首流行音樂。利用電腦編曲自動產生流行音樂組曲，將可提升播放音樂的銜接與流暢感。因此，本研究利用資料探勘技術及音樂編曲理論，將多首音樂重新改編成一首組曲。系統首先將每首音樂分段並找出每首音樂的代表段落。接著，系統根據代表段落間的相似度編排順序。最後，為了達到組曲中音樂段落連接的流暢性，我們以模型訓練的方式在段落連結間加入間奏。系統從訓練資料學習產生旋律發展、和弦進程與節奏的模型，接著分析代表段落的動機、旋律、和弦及節奏，使得組曲編曲後的段落連結更為流暢且完整。本研究以流行音樂鋼琴伴奏曲為測試資料，我們分別邀請三十四位受過音樂訓練與未受音樂訓練的測試者，針對本研究所提出的鋼琴伴奏節奏辨識、代表段落萃取、段落順序編排及間奏產生，評估其效果。實驗結果顯示，本研究所提出的順序編排與間奏產生技術，對於組曲的流暢感，有著相當大的幫助。 In music, a medley is an organized piece composed from segments of existing pieces. Ordering and bridge for connection between segments are the important elements for medley arrangement. Automatic medley arrangement is helpful for playing a set of music continuously which is common in banquet, party, restaurant, shopping mall, etc.. This thesis aims to develop the automatic medley arrangement method by using data mining techniques and music arrangement theory. The proposed method first segments each music and discovers the significant segment from each music. Then, the linear arrangement based on the similarities between significant segments is generated. Finally, in order to connect segments smoothly in the medley, the bridge between two segments is generated and inserted by using model training. Three models, melody progression, chord progression and rhythm models are learned from training data. For the experiments, testing data is collected from popular piano music and thirty-four people are invited to evaluate the effectiveness of the rhythm recognition of accompaniment, the extraction of significant segment, the linear arrangement of segments, and the creation of bridge. Experimental results show that the proposed medley arrangement method performs well.
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Description:	碩士國立政治大學資訊科學學系 94753031 95
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0094753031
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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