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姓名 陳膺任(Chen-Ying Ren) 查詢紙本館藏 畢業系所 資訊工程學系 論文名稱 基於階層式狄氏程序混合模型之音樂情緒標註之研究
(A Study on Hierarchical Dirichlet Process Mixture Model Based Music Emotion Annotation)相關論文 檔案 [Endnote RIS 格式]
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摘要(中) 音樂在人們生活中有著舉足輕重的地位，在資訊數位化且越來越容易保存的現代，
音樂資料庫也更加龐大，因此需要一個自動化的分類方式，來幫助人們快速找到自己需
要的音樂。傳統上可能會以歌手、類型來做為類別，但是音樂真正影響我們的其實是它
所釋放的情感，因此以音樂情緒作為類別來進行標註或檢索的研究，也逐漸受到重視。
傳統上的情緒模型，將每個類別分開建構，但真實世界中情緒的界定並不是那麼清
楚，也就是情緒之間會有一些模糊地帶或是重疊。而我們考慮到這一點，並基於階層式
狄氏程序混合模型(Hierarchical Dirichlet Process Mixture Model)可以共用成分的特性，建
立模型間的連結關係，提出一個音樂情緒標註與檢索系統。同時我們也考慮到共用的特
性可能會造成類別間的混淆，因此我們基於線性鑑別分析(Linear Discriminant Analysis)
的概念，在系統中加入了鑑別性的因素，另外，本系統以一個對應整體情緒成分的權重
向量，來表示每個情緒類別。對於測試資料，我們也提出了三種不同的方法，來產生測
試資料對應的權重向量。而我們利用該權重向量來判斷測試資料是否包含某個情緒。
在實驗部分，其結果顯示我們提出的系統不論在標註或檢索上都有更好的表現，而
我們也將討論不同的計算測試資料權重方法的差異。摘要(英) The development of digital technology has enabled the storage of large collections music
could be. For the convenience of users, some music database applications tag songs with
some class labels. In tradition, music was classified by artist or genre, but the real influence of
music is the emotion which it releases. Therefore, researchers have recently been studying the
music annotation and retrieval method.
In tradition, the model of each emotion was constructed individually, but an emotion
cannot be defined clearly in the real world because the classes of emotions are usually
considered overlapping. Accordingly, this paper proposes an music annotation and retrieval
system that is based on hierarchical Dirichlet process mixture model (HDPMM), whose
components can be shared between each model of emotions. Moreover, an improvement in
HDPMM is proposed by added a discriminant factor to the proposed system based on the
concept of linear discriminant analysis. The proposed system represents an emotion using a
weighting coefficient that is related to a global set of components. Moreover, three methods
are proposed to compute the weighting coefficients of testing data, and using the weighting
coefficient to determine whether the testing data contain certain emotional content or not.
Experimental results show that the proposed system performs well in automatic music
emotion annotation and retrieval tasks. Finally, the evaluation of the three methods for
computing weighting coefficients of testing data is also discussed in the experiments.關鍵字(中) ★ 階層式狄氏程序
★ 音樂情緒辨識關鍵字(英) ★ Hierarchical Dirichlet Process
★ Music Emotion recognition論文目次 摘要........ .................................................................................................................................... i
Abstract... ................................................................................................................................. ii
章節目次 .................................................................................................................................. iii
圖目錄… ................................................................................................................................... v
表目錄… .................................................................................................................................. vi
第一章 緒論 ............................................................................................................................ 1
1.1. 前言 ............................................................................................................................... 1
1.2. 研究動機與目的 ............................................................................................................ 2
1.3. 論文架構與章節概要 .................................................................................................... 3
第二章 相關研究及文獻探討 ................................................................................................ 4
2.1. 音樂情緒特徵 ................................................................................................................ 4
2.1.1. 均方根振幅(Root Mean Square Energy) ....................................................................... 4
2.1.2. 音樂事件密度(Event Density) ......................................................................................... 4
2.1.3. 粗糙度(Roughness) .......................................................................................................... 5
2.1.4. 音調(Chromagram) ......................................................................................................... 5
2.1.5. 大小調(Mode) ................................................................................................................... 6
2.1.6. 過零率(Zero Crossing Rate) ........................................................................................... 6
2.1.7. 梅爾倒頻譜係數(Mel-scale Frequency Cepstral Coefficients, MFCC) ...................... 6
2.2. 音樂情緒分類與分類器方法文獻回顧 ........................................................................ 7
2.2.1. 高斯混合模型（Gaussian Mixture Model, GMM） .................................................... 7
2.2.2. 支持向量機(Support Vector Machine, SVM ) .............................................................. 8
2.2.3. 考量整體情境(Context)的音樂分類方法 ..................................................................... 10
2.2.4. 數值化音樂情緒表示方法 ............................................................................................. 11
iv
第三章 階層式狄氏程序 ...................................................................................................... 13
3.1. 狄氏程序(Dirichlet Process)簡介 .............................................................................. 13
3.2. 狄氏程序建構方法 ...................................................................................................... 13
3.2.1. 截棍程序(Stick-Breaking Process) ............................................................................... 13
3.2.2. 中國餐廳程序(Chinese Restaurant Process) ............................................................... 15
3.3. 狄氏程序混合模型(Dirichlet Process Mixture Model) ............................................ 16
3.4. 階層式狄氏程序(Hierarchical Dirichlet Process)混合模型 .................................... 18
3.4.1. 基於中國連鎖餐廳的後驗取樣 ..................................................................................... 22
3.4.2. Augmented Representation 後驗取樣 ......................................................................... 24
3.4.3. 直接分配(Direct Assignment)後驗取樣 ....................................................................... 25
第四章 音樂情緒標註系統 .................................................................................................. 26
4.1. 簡介(Introduction) ...................................................................................................... 26
4.2. 基於階層式狄氏程序混合模型之音樂情緒標註系統 .............................................. 27
4.3. 結合階層式狄氏程序與稀疏表示方法 ...................................................................... 32
4.4. 考慮鑑別性因素之系統 .............................................................................................. 35
第五章 實驗結果 .................................................................................................................. 37
5.1. 實驗設置與環境 .......................................................................................................... 37
5.2. 音樂情緒標註實驗 ...................................................................................................... 38
5.3. 音樂情緒檢索實驗 ...................................................................................................... 41
第六章 結論及未來研究方向 .............................................................................................. 42
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Applications: A Survey,” Acta Automatica Sinica, vol. 37, no.4, Apr. 2011.指導教授 王家慶(Jia-Ching Wang) 審核日期 2014-8-26 推文 plurk
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