政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32664

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32664

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政大典藏 > College of Informatics > Department of Computer Science > Theses > Item 140.119/32664

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/32664

Title:	基於不同音樂特徵的音樂檢索方法的效果及效率比較 Comparing Music Retrieval Methods with Different Music Features
Authors:	梁敬偉 Liang, Jing Wei
Contributors:	陳良弼 Chen,Arbee L.P. 梁敬偉 Liang, Jing Wei
Keywords:	音樂檢索近似重複樣式音樂分段 music retrieval approximate repeating pattern music segmentation
Date:	2005
Issue Date:	2009-09-17 13:59:56 (UTC+8)
Abstract:	抽取出音樂當中的近似重複樣式來做音樂檢索可以減少要比對的資料量，但是使用者若使用沒有重複的旋律來查詢便會有找不到歌曲的情況。另一方面，將音樂分段成phrase可以減少樹狀索引結構的空間，亦可減少查詢處理時間，但是使用者的查詢若是跨越phrase的，也將影響查詢結果。在本論文中，我們比較了以近似重複樣式與phrase兩種不同的音樂特徵用來做音樂檢索的效果以及效率。根據實驗顯示，使用者的查詢是重複旋律的機會大於單一phrase，所以用近似重複樣式作為音樂查詢比對資料效果是比phrase好的。而在1-D List索引結構下，近似重複樣式的效率也優於phrase。除此之外，本論文也提出了一個新的近似重複樣式抽取方法，實驗證明我們的方法是有效的。 Extract the approximate repeating pattern from music data will decrease the volumes of music data that need to be tested when music retrieve. If the user’s query is not a repeating melody, it can’t retrieve the music that the user wants correctly. In addition, segment the music by phrase will decrease the space that tree-like index structure need, and also decrease the retrieval processing time. If the user’s query is not a single phrase, it will influence the effectiveness of retrieval. In this thesis, we compare the effectiveness and efficiency of music retrieval methods with two different music features (approximate repeating pattern and phrase). According to experiment results, the probability that user’s query is repeating melody is more than the probability that user’s query is a single phrase. Therefore, we are of the opinion that the effectiveness that use approximate repeating pattern to process retrieval is more prominent than the effectiveness that phrase to process retrieval. Furthermore, the efficiency that use approximate repeating pattern to process retrieval is more outstanding than use phrase under 1-D List index structure. Besides, a new approximate repeating pattern extraction method is proposed. Experiment results show that our approximate repeating pattern extraction method can work correctly.
Reference:	[1] B. Blackburn, and D. DeRoure. A tool for content-based navigation of music. In Proceedings of ACM Multimedia 98, September 11-15, 1998. [2] E. Cambouropoulos. The Local Boundary Detection Model (LBDM) and its Application in the Study of Expressive Timing. In Proceedings of International Computer Music Conference, 2001. [3] H.C. Chen, C.H. Lin, and A.L.P. Chen. Music Segmentation by Rhythmic Features and Melodic Shapes. In Proceedings of IEEE International Conference on Multimedia and Expo, 2004. [4] T. Crawford, C.S. Iliopoulos, and R. Raman. String Matching Techniques for Musical Similarity and Melodic Recognition, Computing in Musicology, Vol 11, 73-100,1998. [5] J.S. Downie. The musifind music information retrieval project, phase iii: evaluation of indexing options. In Canadian Association for Information Science proceedings of the 23rd Annual Conference, Connectedness: Information, Systems, People, Organisations, pages 135-46. 1995. [6] A. Ghias, J. Logan, D. Chamberlin, and B. Smith. Query by humming - musical information retrieval in an audio database. In ACM Multimedia 95 - Electronic Proceedings, 1995. [7] D. Gusfield. Algorithms on Strings, Trees, and Sequences, Cambridge University Press, 1997. [8] 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. [9] J.L. Hsu, C.C. Liu, and A.L.P. Chen. Discovering Non-trivial Repeating Patterns in Music Data, IEEE Transactions on Multimedia, 2001. [10] W. Lee, and A.L.P. Chen. Efficient Multi-Feature Index Structures for Music Data Retrieval. In Proceedings of SPIE Conference on Storage and Retrieval for Image and Video Databases, 2000. [11] K. Lemström, and E. Ukkonen. Including interval encoding into edit distance based music comparison and retrieval. Proc. of the AISB`2000 Symposium on Creative & Cultural Aspects and Applications of AI & Cognitive Science, pp. 53-60, University of Birmingham, 2000. [12] C.C. Liu, J.L. Hsu, and A.L.P. Chen. An Approximate String Matching Algorithm for Content-based Music Data Retrieval. In Proceedings of International Conferences on Multimedia Computing and Systems, 1999. [13] N.H. Liu, Y.H. Wu, and A.L.P. Chen. An Efficient Approach to Extracting Approximate Repeating Patterns in Music Databases, In Proceedings of International Conference on Database Systems for Advanced Applications, 2005. [14] MIDI Manufacturers Association (MMA), MIDI 1.0 Specification, http://www.midi.org/. [15] R.J. McNab, L.A. Smith, I.H. Witten, C.L. Henderson, and S.J. Cunningham. Towards the digital music library: Tune retrieval from acoustic input. In Digital Libraries Conference, 1996. [16] M. Mongeau, and D. Sankoff. Comparison of musical sequences. Computers and the Humanities 24 161-175, 1990. [17] R.E. Prather, Harmonic analysis from the computer representation of a musical score. Communication of the ACM. vol. 39, no. 12, Dec. 1996, pp. 119, 1996. [18] P.Y. Rolland. FlExPat: Flexible Extraction of Sequential Patterns, In Proceedings of the IEEE International Conference on Data Mining, 2001. [19] H.H. Shih, S.S. Narayanan, and C.C.J. Kuo. Automatic Main Melody Extraction From MIDI Files with a Modified Lempel-Ziv Algorithm, In Proceedings of International Symposium on Intelligent Multimedia, Video and Speech Processing, 2001. [20] A. Uitdenbogerd, and J. Zobel. Manipulation of music for melody matching. In Proceedings of ACM Multimedia 98, September 11-15, 1998. [21] A. Uitdenbogerd, and J. Zobel. Melodic Matching Techniques for Large Music Databases, In Proceedings of the ACM Multimedia Conference, 1999. [22] 王加元, 音樂容錯查詢-利用Approximate String Matching, 政治大學資訊科學系專題報告, 2005.
Description:	碩士國立政治大學資訊科學學系 93753034 94
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0093753034
Data Type:	thesis
Appears in Collections:	[Department of Computer Science ] Theses

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