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題名 以主題為基礎的音樂結構性分析
Theme-Based Music Structural Analysis
作者 何旻璟
Ho, Min-ching
貢獻者 沈錳坤
Shan, Man-Kwan
何旻璟
Ho, Min-ching
關鍵詞 音樂結構分析
主題式
動機
動機處理
分段
Music Structural Analysis
Theme-based
Motive
Motivic treatment
Segment
日期 2004
上傳時間 17-Sep-2009 14:06:30 (UTC+8)
摘要 音樂分段在研究音樂分析相關的領域是很重要的研究題目。音樂的分段可以提供作音樂結構分析、音樂瀏覽、音樂內容查詢與音樂摘要等應用。本論文的研究目的就是對音樂作自動分段，以幫助使用者能快速瀏覽音樂的內容。因此，我們針對音樂的主題作主題式的分段。
音樂的主題是取決於作曲者的動機，動機是構成音樂主題的基本因素。為了能夠以主題為基礎作音樂分段，我們必須找出決定音樂主題的因素。動機會有規則性的出現在整首音樂當中，所以我們可以利用動機出現的規則來探勘音樂的動機。
我們提出一個以主題對音樂作分段的方法，總共分為四個主要的步驟。第一，我們從原始的音樂資料擷取出主旋律的部分。第二，將主旋律做粗略分段。我們利用探勘Non-trivial重複樣式的技術[17]，來找出粗略段落。第三，從粗略段落中探勘動機。我們利用Stein所提出來的動機變化規則，修改傳統探勘重複序列的方法，做動機的探勘。最後，我們利用探勘出來的動機對主旋律作精細分段。我們針對MIDI音樂檔案利用提出來的方法，實做出一個系統，找出音樂的主題段落。
先前研究在評估實驗結果時，多採用Precision與Recall去評估實驗的結果。然而，這樣的評估方法並不能表現出實驗結果與正確答案之間的相似程度。所以我們提出新的評估方法，根據實驗結果與正確答案之間的相似程度來評估實驗的準確率。根據實驗結果顯示，我們的方法準確率約65%。
Music segmentation is one of the important issues in music analysis. Music segmentation can be utilized for music structure analysis, music browsing, content-based music retrieval, and music summarization. In this theis, we proposed a music segmentation method based on the music theme to provide users the capability to browse music segments by theme.
Motives, the concepts of the composer, are the basic elements of music themes. Music themes were constructed by motives. In order to segment music by themes, we have to discover motives. Most motives repeated in the music by some motivic treatment rules. Therefore, motives can be discovered by these rules.
We proposed the theme segmentation method. There are four steps. Firstly, we extract main melody from original music. In the second step, rough segments are generated from main melody by mining non-trivial repeating patterns. Then, motives are detected from rough segments. We modify the mining algorithm for discovering frequent patterns by applying motivic treatment rules proposed by Stein. Finally, we segment main melody based on the generated motives. Moreover, a system for segmentation of music in MIDI format was implemented.
Concerning the effectiveness evaluation of music segmentation, precision and recall are used in previous research. We proposed an effectiveness measure and corresponding algorithm to evaluate the accuracy of music segmentation. Experimental results show that our proposed music segmentation method achieves 65% accuracy.
參考文獻 [1]M. A. Bartsch and G. H. Wakefield, “To Catch a Chorus: Using Chroma-Based Representations for Audio Thumbnailing,” Proc. of Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA’01, 2001.
[2]R. Bod, “A Memory-Based Model for Music Analysis: Challenging the Gestalt Principles,” Journal of New Music Research, Vol. 31, pp. 27-37, 2001.
[3]E. Cambouropoulos, “A Formal Theory for the Discovery of Local Boundaries in a Melodic Surface,” Proc. of the III Journees d` Informatique Musicale, 1996.
[4]E. Cambouropoulos, “The Local Boundary Detection Model (LBDM) and its Application in the Study of Expressive Timing,” Proc. of the International Computer Music Conference, ICMC’01, 2001.
[5]W. Chai and B. Vercoe, “Folk Music Classification Using Hidden Markov Models,” Proc. of the International Conference on Artificial Intelligence, ICAI’01, 2001.
[6]W. Chai and B. Vercoe, “Music Thumbnailing via Structural Analysis,” Proc. of ACM International Conference on Multimedia, MM’03, 2003.
[7]W. Chai and B. Vercoe, “Structural Analysis of Musical Signals for Indexing and Thumbnailing,” Proc. of ACM/IEEE Joint Conference on Digital Libraries, JCDL’03, 2003.
[8]Y. J. Chen, “A Fast Repeating Pattern Finding Algorithm for Music Data: A Human Perceptive Approach,” Master Thesis, Department of Electrical Engineering, National Cheng Kung University, Taiwan, 2004.
[9]D. Conklin and C. Anagnostopoulou, “Representation and Discovery of Multiple Viewpoint Patterns,” Proc. of the International Computer Music Conference, ICMC’01, 2001.
[10]R. B. Dannenberg, “A Brief Survey of Music Representation Issues, Techniques and Systems,” Computer Music Journal, Vol. 17, No. 3, pp. 20-30, 1993.
[11]R. B. Dannenberg, B. Thom, and D.Watson, “A Machine Learning Approach to Musical Style Recognition,” Proc. of International Computer Music Conference, ICMC’97, 1997.
[12]C. David, Virtual Music : Computer Synthesis of Musical Style, MIT Press, 2001.
[13]S. Dixon, E. Pampalk, and G. Widmer, “Classification of Dance Music by Periodicity Patterns,” Proc. of International Symposium on Music Information Retrieval, ISMIR’03, 2003.
[14]M. Grachten, J. L. Arcos and R. L. Mántaras, “Melodic Similarity: Looking for a Good Abstraction Level,” Proc. of International Symposium on Music Information Retrieval, ISMIR’04, 2004.
[15]K. Hirata and S. Matsuda, “Interactive Music Summarization based on GTTM,” Proc. of International Symposium on Music Information Retrieval, ISMIR’02, 2002.
[16]J. L. Hsu, “Content-based Music Information Retrieval and Analysis,” Ph.D. Thesis, Department of Computer Science, National Tsing Hua University, Taiwan, 2001.
[17]J. L. Hsu, C. C. Liu, and A. L. P. Chen, “Discovering Nontrivial Repeating Patterns in Music Data,” IEEE Transactions on Multimedia, Vol. 3, No. 3, pp. 311-325, 2001.
[18]J. L. Hsu, C. C. Liu, and A. L. P. Chen, “Efficient Repeating Pattern Finding in Music Database,” Proc. of Conference on Information and Knowledge Management, CIKM’98, 1998.
[19]C. S. Iliopoulos, K. Lemström, M. Nuyad, and Y. J. Pinzón, “Evolution of Musical Motifs in Polyphonic Passages,” Proc. of Symposium on AI and Creativity in Arts and Science, AISB’02, 2002.
[20]C. S. Iliopoulos, T. Lecroq, L. Mouchard, and Y. Z. Pinzon, “Computing Approximate Repetitions in Musical Sequence,” International Journal of Computer Mathematics, Vol.3, No. 1&2, 2002.
[21]J. S. R. Jang, H. R. Lee, C. H. Yeh, “A Query-by-Tapping System for Music Retrieval,” Proc. of IEEE Pacific-Rim Conference on Multimedia, 2001.
[22]M. H. Jian, C. H. Lin, and A. L. P. Chen, “Perceptual Analysis for Music Segmentation,” Proc. SPIE Storage and Retrieval Methods and Applications for Multimedia, 2004.
[23]I. Koprinska and S. Carrato, “Temporal Video Segmentation: A Survey,” Signal Processing Image Communication, Vol. 16, No. 5, pp. 477-500, 2001.
[24]F. F. Kuo, “Melody Style Mining Using Chord Features,” Master Thesis, Department of Computer Science, National Chengchi University, Taiwan, 2003.
[25]O. Lartillot, “Discovering Musical Patterns through Perceptive Heuristics,” Proc. of International Symposium on Music Information Retrieval, ISMIR’03, 2003.
[26]O. Lartillot, S. Dubcdv, G. Assayag, and G. Bejerano, “Automatic Modeling of Music Style,” Proc. Of International Computer Music Conference, ICMC, 2002.
[27]F. Lerdahl and R. Jackendoff, A Generative Theory of Tonal Music, MIT Press, 1983.
[28]C. C. Liu, J. L. Hsu, and A. L. P. Chen, “Efficient Theme and Non-Trivial Repeating Pattern Discovering in Music Databases,” Proc. of International Conference on Data Engineering, ICDE’99, 1999.
[29]B. Logan and S. Chu, “Music Summarization Using Key Phrases,” Proc. of International Conference on Speech, Acoustics, and Signal Processing, ICASSP’00, 2000.
[30]L. Lu, H. You, and H.J. Zhang, “A New Approach to Query by Humming In Music Retrieval,” Proc. of IEEE International Conference on Multimedia and Expo, ICME’01, 2001.
[31]K. D. Martin, E. D. Scheirer, and B. L. Vercoe, “Music content analysis through Models of Audition,” Proc. of ACM Multimedia Workshop on Content-Based Processing of Music, 1998.
[32]C. Meek and W. P. Birmingham, “Automatic Thematic Extractor,” Proc. of International Symposium on Music Information Retrieval, ISMIR’01, 2001.
[33]C. Meek and W. P. Birmingham, “Automatic Thematic Extractor,” Journal of Intelligent Information Systems, Vol. 21, No. 1, pp. 9-33, 2003.
[34]D. Meredith, K. Lemström, and G. A. Wiggins, “Algorithm for Discover Repeating Patterns in Multidimensional Representations of Polyphonic Music,” Journal of New Music Research, Vol.31, No.4, pp. 321-345, 2002.
[35]J. Pickens, “Feature Selection for Polyphonic Music Retrieval,” Proc. of the ACM International Conference on Research and Development in Information Retrieval, SIGIR’01, 2001.
[36]D. Rizo, J. M. Iñesta, F. Moreno-Seco, “Tree-structured Representation of Musical Information,” Iberian Conference on Pattern Recognition and Image Analysis, 2003.
[37]M. K. Shan, F. F. Kuo, and M. F. Chen, “Music Style Mining and Classification by Melody,” Proc. of IEEE International Conference on Multimedia and Expo, ICME’02, 2002.
[38]M. K. Shan and F. F. Kuo, “Music Style Mining and Classification by Melody,” IEICE Transactions on Information and System, Vol. E86-D, No. 4, 2003.
[39]H. H. Shih, S. S. Narayanan, and C. C. J. Kuo, “A Dictionary Approach to Repeating Pattern Finding in Music,” Proc. of IEEE International Conference on Multimedia and Expo, ICME’01, 2001.
[40]C. Spevak, B. Thom, and Karin Höthker, “Evaluating Melodic Segmentation,” Proc. of International Conference on Music and Artificial Intelligence, ICMAI’02, 2002.
[41]L. Stein, Structure & Style：The Study and Analysis of Musical Forms, Summy-Birchard Music, 1979.
[42]D. Temperley, The Cognition of Basic Musical Structires, MIT press, 2001.
[43]B. Thom, “BoB: An Improvisational Music Companion,” Ph.D. Thesis, Department of Computer Science, Carnegie Mellon University, USA, 2001.
[44]B. Thom, C. Spevak, and K. Höthker, “Melodic Segmentation: Evaluating the Performance of Algorithms and Musical Experts,” Proc. of the International Computer Music Conference, ICMC’02, 2002.
[45]Y. H. Tseng, “Content-Based Retrieval for Music Collections,” Proc. of ACM Conference on Research and Development in Information Retrieval, SIGIR’99, 1999.
[46]A. L. Uitdenbogerd and J. Zobel, “Manipulation of Music For Melody Matching,” Proc. of ACM International Conference on Multimedia, MM’98, 1998.
[47]A. L. Uitdenbogerd and J. Zobel, “Melodic Matching Techniques for Large Music Databases,” Proc. of ACM International Conference on Multimedia, MM’99, 1999.
[48]T. Weyde, “Integrating Segmentation and Similarity in Melodic Analysis,” Proc. of the International Conference on Music Perception and Cognition, ICMPC’02, 2002.
[49]C. Xu, Y. Zhu, and Q. Tian, “Automatic Music Summarization Based on Temporal, Spectral and Cepstral Features,” Proc. of IEEE International Conference on Multimedia and Expo, ICME’02, 2002.
[50]M. Zinn and R. Hogenson, Basic of Music: Opus 1, Schirmer books, 1994.
描述 碩士
國立政治大學
資訊科學學系
91753008
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0917530081
資料類型 thesis
dc.contributor.advisor 沈錳坤zh_TW
dc.contributor.advisor Shan, Man-Kwanen_US
dc.contributor.author (Authors) 何旻璟zh_TW
dc.contributor.author (Authors) Ho, Min-chingen_US
dc.creator (作者) 何旻璟zh_TW
dc.creator (作者) Ho, Min-chingen_US
dc.date (日期) 2004en_US
dc.date.accessioned 17-Sep-2009 14:06:30 (UTC+8)-
dc.date.available 17-Sep-2009 14:06:30 (UTC+8)-
dc.date.issued (上傳時間) 17-Sep-2009 14:06:30 (UTC+8)-
dc.identifier (Other Identifiers) G0917530081en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/32709-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊科學學系zh_TW
dc.description (描述) 91753008zh_TW
dc.description (描述) 93zh_TW
dc.description.abstract (摘要) 音樂分段在研究音樂分析相關的領域是很重要的研究題目。音樂的分段可以提供作音樂結構分析、音樂瀏覽、音樂內容查詢與音樂摘要等應用。本論文的研究目的就是對音樂作自動分段，以幫助使用者能快速瀏覽音樂的內容。因此，我們針對音樂的主題作主題式的分段。
音樂的主題是取決於作曲者的動機，動機是構成音樂主題的基本因素。為了能夠以主題為基礎作音樂分段，我們必須找出決定音樂主題的因素。動機會有規則性的出現在整首音樂當中，所以我們可以利用動機出現的規則來探勘音樂的動機。
我們提出一個以主題對音樂作分段的方法，總共分為四個主要的步驟。第一，我們從原始的音樂資料擷取出主旋律的部分。第二，將主旋律做粗略分段。我們利用探勘Non-trivial重複樣式的技術[17]，來找出粗略段落。第三，從粗略段落中探勘動機。我們利用Stein所提出來的動機變化規則，修改傳統探勘重複序列的方法，做動機的探勘。最後，我們利用探勘出來的動機對主旋律作精細分段。我們針對MIDI音樂檔案利用提出來的方法，實做出一個系統，找出音樂的主題段落。
先前研究在評估實驗結果時，多採用Precision與Recall去評估實驗的結果。然而，這樣的評估方法並不能表現出實驗結果與正確答案之間的相似程度。所以我們提出新的評估方法，根據實驗結果與正確答案之間的相似程度來評估實驗的準確率。根據實驗結果顯示，我們的方法準確率約65%。		zh_TW
dc.description.abstract (摘要) Music segmentation is one of the important issues in music analysis. Music segmentation can be utilized for music structure analysis, music browsing, content-based music retrieval, and music summarization. In this theis, we proposed a music segmentation method based on the music theme to provide users the capability to browse music segments by theme.
Motives, the concepts of the composer, are the basic elements of music themes. Music themes were constructed by motives. In order to segment music by themes, we have to discover motives. Most motives repeated in the music by some motivic treatment rules. Therefore, motives can be discovered by these rules.
We proposed the theme segmentation method. There are four steps. Firstly, we extract main melody from original music. In the second step, rough segments are generated from main melody by mining non-trivial repeating patterns. Then, motives are detected from rough segments. We modify the mining algorithm for discovering frequent patterns by applying motivic treatment rules proposed by Stein. Finally, we segment main melody based on the generated motives. Moreover, a system for segmentation of music in MIDI format was implemented.
Concerning the effectiveness evaluation of music segmentation, precision and recall are used in previous research. We proposed an effectiveness measure and corresponding algorithm to evaluate the accuracy of music segmentation. Experimental results show that our proposed music segmentation method achieves 65% accuracy.		en_US
dc.description.tableofcontents 中文摘要 i
英文摘要 iii
誌謝 v
目錄 vii
圖目錄 x
表目錄 xiii
第一章 概論 1
1.1 動機 1
1.2 背景 2
1.3 MIDI 格式 5
1.4 論文架構 7
第二章 相關研究 8
2.1 音樂分段 8
2.2 主題萃取 13
2.3 音樂摘要 13
2.4 音樂樣式探勘 15
2.4.1 探勘精確重複樣式 15
2.4.2 探勘趨近重複樣式 17
第三章 音樂分段 19
3.1 主旋律的萃取與表示法 22
3.2 粗略分段 27
3.2.1 以Non-trivail重複樣式作粗略分段 28
3.3 動機偵測 31
3.4 精確分段 41
第四章 系統實作 44
4.1 調性音階 44
4.2 音長區間量化 45
4.3 動機整合 46
4.4 MIDI parser 46
4.5 轉換MIDI格式 47
4.6 系統介面 47
第五章 實驗 50
5.1 實驗資料與實驗設計 50
5.2 實驗評估方法 51
5.3 實驗結果 54
第六章 結論與未來研究 56
6.1 結論 56
6.2 未來研究 57
參考文獻 58		zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0917530081en_US
dc.subject (關鍵詞) 音樂結構分析zh_TW
dc.subject (關鍵詞) 主題式zh_TW
dc.subject (關鍵詞) 動機zh_TW
dc.subject (關鍵詞) 動機處理zh_TW
dc.subject (關鍵詞) 分段zh_TW
dc.subject (關鍵詞) Music Structural Analysisen_US
dc.subject (關鍵詞) Theme-baseden_US
dc.subject (關鍵詞) Motiveen_US
dc.subject (關鍵詞) Motivic treatmenten_US
dc.subject (關鍵詞) Segmenten_US
dc.title (題名) 以主題為基礎的音樂結構性分析zh_TW
dc.title (題名) Theme-Based Music Structural Analysisen_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1]M. A. Bartsch and G. H. Wakefield, “To Catch a Chorus: Using Chroma-Based Representations for Audio Thumbnailing,” Proc. of Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA’01, 2001.zh_TW
dc.relation.reference (參考文獻) [2]R. Bod, “A Memory-Based Model for Music Analysis: Challenging the Gestalt Principles,” Journal of New Music Research, Vol. 31, pp. 27-37, 2001.zh_TW
dc.relation.reference (參考文獻) [3]E. Cambouropoulos, “A Formal Theory for the Discovery of Local Boundaries in a Melodic Surface,” Proc. of the III Journees d` Informatique Musicale, 1996.zh_TW
dc.relation.reference (參考文獻) [4]E. Cambouropoulos, “The Local Boundary Detection Model (LBDM) and its Application in the Study of Expressive Timing,” Proc. of the International Computer Music Conference, ICMC’01, 2001.zh_TW
dc.relation.reference (參考文獻) [5]W. Chai and B. Vercoe, “Folk Music Classification Using Hidden Markov Models,” Proc. of the International Conference on Artificial Intelligence, ICAI’01, 2001.zh_TW
dc.relation.reference (參考文獻) [6]W. Chai and B. Vercoe, “Music Thumbnailing via Structural Analysis,” Proc. of ACM International Conference on Multimedia, MM’03, 2003.zh_TW
dc.relation.reference (參考文獻) [7]W. Chai and B. Vercoe, “Structural Analysis of Musical Signals for Indexing and Thumbnailing,” Proc. of ACM/IEEE Joint Conference on Digital Libraries, JCDL’03, 2003.zh_TW
dc.relation.reference (參考文獻) [8]Y. J. Chen, “A Fast Repeating Pattern Finding Algorithm for Music Data: A Human Perceptive Approach,” Master Thesis, Department of Electrical Engineering, National Cheng Kung University, Taiwan, 2004.zh_TW
dc.relation.reference (參考文獻) [9]D. Conklin and C. Anagnostopoulou, “Representation and Discovery of Multiple Viewpoint Patterns,” Proc. of the International Computer Music Conference, ICMC’01, 2001.zh_TW
dc.relation.reference (參考文獻) [10]R. B. Dannenberg, “A Brief Survey of Music Representation Issues, Techniques and Systems,” Computer Music Journal, Vol. 17, No. 3, pp. 20-30, 1993.zh_TW
dc.relation.reference (參考文獻) [11]R. B. Dannenberg, B. Thom, and D.Watson, “A Machine Learning Approach to Musical Style Recognition,” Proc. of International Computer Music Conference, ICMC’97, 1997.zh_TW
dc.relation.reference (參考文獻) [12]C. David, Virtual Music : Computer Synthesis of Musical Style, MIT Press, 2001.zh_TW
dc.relation.reference (參考文獻) [13]S. Dixon, E. Pampalk, and G. Widmer, “Classification of Dance Music by Periodicity Patterns,” Proc. of International Symposium on Music Information Retrieval, ISMIR’03, 2003.zh_TW
dc.relation.reference (參考文獻) [14]M. Grachten, J. L. Arcos and R. L. Mántaras, “Melodic Similarity: Looking for a Good Abstraction Level,” Proc. of International Symposium on Music Information Retrieval, ISMIR’04, 2004.zh_TW
dc.relation.reference (參考文獻) [15]K. Hirata and S. Matsuda, “Interactive Music Summarization based on GTTM,” Proc. of International Symposium on Music Information Retrieval, ISMIR’02, 2002.zh_TW
dc.relation.reference (參考文獻) [16]J. L. Hsu, “Content-based Music Information Retrieval and Analysis,” Ph.D. Thesis, Department of Computer Science, National Tsing Hua University, Taiwan, 2001.zh_TW
dc.relation.reference (參考文獻) [17]J. L. Hsu, C. C. Liu, and A. L. P. Chen, “Discovering Nontrivial Repeating Patterns in Music Data,” IEEE Transactions on Multimedia, Vol. 3, No. 3, pp. 311-325, 2001.zh_TW
dc.relation.reference (參考文獻) [18]J. L. Hsu, C. C. Liu, and A. L. P. Chen, “Efficient Repeating Pattern Finding in Music Database,” Proc. of Conference on Information and Knowledge Management, CIKM’98, 1998.zh_TW
dc.relation.reference (參考文獻) [19]C. S. Iliopoulos, K. Lemström, M. Nuyad, and Y. J. Pinzón, “Evolution of Musical Motifs in Polyphonic Passages,” Proc. of Symposium on AI and Creativity in Arts and Science, AISB’02, 2002.zh_TW
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