研究生: |
梁文翰 Wen-han Liang |
---|---|
論文名稱: |
以片段抽取和模糊比對為基礎的音樂旋律檢索方法 Music Information Retrieval Based On Pattern Extraction and Fuzzy Matching |
指導教授: |
林伯慎
Bor-shen Lin |
口試委員: |
楊傳凱
Chuan-kai Yang 古鴻炎 Hung-yan Gu |
學位類別: |
碩士 Master |
系所名稱: |
管理學院 - 資訊管理系 Department of Information Management |
論文出版年: | 2011 |
畢業學年度: | 100 |
語文別: | 中文 |
論文頁數: | 69 |
中文關鍵詞: | 音樂檢索 、N-gram 、前綴樹 、模糊比對 |
外文關鍵詞: | Music Retrieval, N-gram, Prefix Tree, Fuzzy Match |
相關次數: | 點閱:191 下載:7 |
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一般內涵式音樂資訊檢索的作法,是先將查詢旋律轉為特徵序列,再與資料庫所有的樂曲做動態比對以找出目標歌曲。但是這種作法會隨著資料庫音樂數量或特徵序列長度的增加導致搜尋時間線性增加,而難以達到即時查詢的目的。本論文抽取出音樂文件集中具有重複性和鑑別力的旋律片段,建立「旋律片語辭典」。如此,便能將文件檢索的方法應用至音樂檢索中。實驗結果顯示,透過「旋律片語辭典」的協助進行音樂檢索,能在短時間之內找到目標樂曲。在不過度犧牲平均排名的情況下,經由篩選片語辭典去除其中較不具鑑別力的旋律片段,可以大幅度降低比對時間。另外,對於哼唱檢索可能出現的錯音問題,我們提出片段擴展的方法,能夠有效提升目標歌曲在哼唱搜索時的平均排名。本論文也提出並比較了兩種查詢旋律和樂曲間的模糊比對公式以計算的相似度。在目標歌曲的檢索上,我們發現非對稱距離比對稱距離效能為佳。在非目標歌曲的檢索上,模糊距離度量比起精確距離度量,所找出的非目標歌曲與目標歌曲間有更高的平均旋律相似度;也就是說,模糊比對有助於找到和目標歌曲旋律近似的非目標歌曲。這樣的特性使音樂檢索系統可應用於探索或分析具有在旋律上具相關性的樂曲。
Conventional content-based music information retrieval (CBMIR) approaches usually convert queries into feature sequences and dynamically compare them with those of the documents in the music database based on dynamic programming so as to find out the target document. However, with the increase of the amount of the collections or the dimension of the feature sequences, the required computation time increases linearly, which makes real-time retrieval for large music database difficult. This paper proposes a retrieval method based on repeated and discriminative melody patterns extracted from music collections, through which the techniques of document retrieval can be applied to CBMIR. Experimental results show that, the search time is relatively short, and can be further reduced by eliminating less discriminative patterns without sacrificing the performance heavily. In addition, two fuzzy measures, denoted as symmetric and asymmetric measures respectively, were proposed in this paper for ranking the retrieved documents. It could be found that the asymmetric one achieves better average ranking for the target document. Additionally, for asymmetric measure, higher average similarity of the non-target documents with the target document can be achieved when compared with symmetric measure and conventional non-fuzzy cosine similarity. The proposed fuzzy matching is helpful for discovering novel and relevant music documents that have patterns similar to the target document, and can potentially be applied to the analysis of music documents with melodic relevance.
[1] Padama Iyer: Music Information Retrieval, Vishvabharti Publications, New Delhi, (2004).
[2] Michael A. Casey, Remco Valtkamp, Masataka Goto, Marc Leman, Christophe Rhodes, and Malcolm Slaney: “Content-Based Music Information Retrieval: Current Directions and Future Challenges”, Proceedings of the IEEE, Vol. 96, No. 4, pp. 668–696, (2008).
[3] 梁敬偉,「基於不同音樂特徵的音樂檢索方法的效果及效率比較」,碩士論文,國立政治大學,台北 (2006)。
[4] Hewijin Christine Jiau and Chuan-Wang Chang: “A Dual Ternary Indexing Approach for Music Retrieval System”, Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.3, pp. 227-233, (2008).
[5] Jue Hou, et al: “Effectiveness of N-Gram fast Match for Query-by-Humming System”, IEEE International Conference on Multimedia and Expo (ICME 2009), pp. 1310-1313.
[6] Aurora Marsye and Mirna Adriani, “Searching Polyphonic Indonesian Folksongs Based on N-gram Indexing Technique”, The Fifth Asia Information Retrieval Symposium (AIRS 2009), LNCS Series, pp. 387-396, (2009).
[7] Alexandra Uitdenbogerd and Justin Zobel. “Melodic Matching Techniques for Large Music Databases”, In: Proceedings of the ACM Multimedia Conference, (1999).
[8] S. Doraisamy, “Polyphonic Music Retrieval: The N-Gram Approach”, PhD Thesis, Imperial College London, (2004).
[9] Alexandra Uitdenbogerd and Justin Zobel. “Manipulation of Music for Melody Matching”, In: Proceeding ACM International Multimedia Conferences, pp. 235-240. ACM Press, Bristol, (1998).
[10] A. Ghias, J. Logan, D. Chamberlin and B. C. Smith, “Query by humming: Musical information retrieval in an audio database” In Proc ACM Int'l Conf. on Multimedia, ACM, pp. 231-236, (1995).
[11] J.L. Hsu, C.C. Liu, and A.L.P. Chen: “Discovering Non-trivial Repeating Patterns in Music Data”, IEEE Transactions on Multimedia, (2001).
[12] Tom Collins, Jeremy Thurlow, Robin Laney, Alistair Willis, and Paul H. Garthwaite: “A Comparative Evaluation of Algorithms for Discovering Translational Patterns In Baroque Keyboard Works”, Proceedings of International Society for Music Information Retrieval Conference, pp. 3-8, (2010).
[13] J.L. Hsu, A.L.P. Chen, and H.C. Chen: “Finding Approximate Repeating Patterns from Sequence Data”, In Proceedings of International Symposium on Music Information Retrieval, (2004).
[14] Suyoto, I.S., Uitdenbogerd, A.L.: “Effectiveness of Note Duration Information for Music Retrieval”, Proceeding Tenth International Conference on Database Systems for Advanced Application, pp. 265-275. Springer, Beijing, (2005).
[15] Uitdenbogerd, A. L.; Zobel, Justin: “Music ranking techniques evaluated.”, Twenty-Fifth Australasian Computer Science Conference, (2002).
[16] D. Meredith, K. Lemstrom, and G.A. Wiggins, "Algorithms for discovering repeated patterns in multidimensional representations of polyphonic music", Journal of New Music Research, vol. 31, no. 4, pp. 321-345, (2002).
[17] Yu-lung Lo, Ho-cheng Yu, and Mei-chin Fan, "FastPET: A Fast Non-trivial Repeating Pattern Extracting Technique for Music Data," 2001 National Computer Symposium, Taipei, pp. 43-52, (2001).
[18] 羅有隆、游合成,「以線性記憶體空間發現音樂資料中非不重要重覆片段之技術」,2002 年第六屆資訊管理學術暨警政資訊實務研討會,中央警察大學, 桃園,pp. 341-350 (2002)。
[19] Shyamala Doraisamy and Stefan Ruger, "Robust Polyphonic Music Retrieval with N-grams," Journal of Intelligent Information Systems, vol. 21, no. 1, pp. 53-70, (2003).
[20] C. C. Liu, A. J. L. Hsu, and A. L. P. Chen, “1-D List: An Approximate String Matching Algorithm for Content-Based Music Data Retrieval,” IEEE Int. Conf. on Multimedia Computing and Systems, vol.1, pp.451-456, (1999).
[21] T. C. Chou, A. L. P. Chen, and C. C. Liu, “Music Databases: Indexing Techniques and Implementation,” International Workshop on Multimedia Data Base Management Systems, (1996).
[22] Ian Knopke and Frauke Ju rgensen: “A System for Identifying Common Melodic Phrases in the Masses of Palestrina”, Journal of New Music Research, Vol. 38, No. 2, pp. 171-181, (2009).
[23] H. Sakoe and S. Chiba, "Dynamic programming algorithm optimization for spoken word recognition", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-26, no. 1, pp.43-49, (1978).
[24] Gerard Salton, Christopher Buckley, “Term-weighting approaches in automatic text retrieval”, Information Processing and Management: an International Journal, vol. 24 no. 5, pp. 513-523, (1988).
[25] Jang, J.-S. Roger and Gao, Ming-Yang, “A Query-by-Singing System based on Dynamic Programming”, International Workshop on Intelligent Systems Resolutions (the 8th Bellman Continuum), Hsinchu, Taiwan, pp. 85-89, (2000).