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| Title: | 利用和絃特徵探勘音樂旋律曲風之研究
Melody Style Mining Using Chord Features |
| Authors: | 郭芳菲
Kuo, Fang-Fei |
| Contributors: | 沈錳坤
Shan, Man-Kwan
郭芳菲
Kuo, Fang-Fei |
| Keywords: | 音樂曲風探勘
音樂內容擷取
個人化技術
資料探勘
Music Style Mining
Content-Based Music Retrieval
Personalization
Data Mining |
| Date: | 2002 |
| Issue Date: | 2009-09-17 13:52:39 (UTC+8) |
| Abstract: | 隨著數位多媒體技術的進步，越來越多的音樂以數位化的方式來儲存，數位音樂的檢索成為重要的研究領域之一。以內容為主的音樂檢索(Content-Based Music Retrieval, CBMR)能讓使用者直接利用音樂的內容做檢索，而非傳統以音樂的metadata查詢的方法。目前有關CBMR的研究，常見的查詢方式包括哼歌、唱歌或打拍子等。但是，這些方法都會因為查詢者缺乏音樂訓練而無法正確表達出想查詢的音樂，影響查詢效果。
人們常常會根據曲風將音樂分類，音樂曲風的探勘將有助於CBMR的研究。本篇論文主要目的在結合多媒體與資料探勘的技術，從大量MIDI音樂中，作音樂曲風的探勘及分類，並將曲風探勘的技術應用在個人化音樂推薦、音樂風格檢索及音樂風格瀏覽上。
在本論文的第一部份，音樂曲風探勘分類的研究，包括了三個研究議題：音樂特徵的粹取、頻繁樣式的探勘及曲風的分類。我們利用和絃作為音樂的特徵，根據和聲學的原理，從MIDI音樂中找出主旋律搭配的和絃。粹取出和絃後，我們研究不同的和絃特徵表示法與其頻繁樣式探勘演算法。針對所探勘出的頻繁樣式，我們修改associated classification演算法，以應用在音樂曲風的分類上。此外，不同的曲風，其風格的多樣性也不同。因此，為了提高分類的效果，我們提出Single-Type Variant-Support (STVS) 與Multi-Type Variant-Support (MTVS) classification演算法，使得分類規則中允許多種特徵表示與不同的最小支持度。
在本篇論文的第二部分，我們應用曲風探勘的技術，提出了個人化音樂推薦的機制。針對使用者對音樂風格的喜好，將新的音樂推薦給使用者。系統根據使用者對資料庫中音樂的存取行為，學習使用者在音樂曲風上的偏好，產生個人化的2-way preference classifier，以推薦符合使用者喜好的音樂。
第三部分為音樂曲風的檢索。目前大部分的CBMR系統中，使用者僅能尋找已經聽過的音樂。然而，使用者想查詢的很可能是沒聽過，但曲風感覺類似的音樂。針對上述的問題，我們提出了以音樂曲風作檢索的新方法。同時，我們提出四種曲風查詢的描述方式，並且利用音樂風格探勘與分類的技術產生的分類規則計算曲風的相似度，最後依照曲風的相似程度產生檢索結果。
本篇論文的最後一部分為音樂風格的分群。音樂風格的分群有助於瀏覽大量的音樂資料。我們利用和絃為特徵，針對不同的特徵表示方法，提出相似度的計算方式。我們將數種分群演算法應用於音樂風格的分群上，並比較各種分類演算法與不同的音樂特徵與表示法的分群效果。
With the development of multimedia technology, digital music is now in widespread use. Content-based music retrieval (CBMR) has attracted much interest in recent years. CBMR allows users query by music content rather than metadata. However, even with the capability of query by humming, the effectiveness of CBMR system suffers from the ability of query content expression for people without music training.
Music style is one of the features that people used to classify music. Discovery of music style is helpful for the design of content-based music retrieval systems. In this thesis, we investigate the mining techniques of music style by melody from a collection of MIDI music and apply the mining techniques to three applications, personalized music filtering, music retrieval by melody style and music style browsing.
In the first part, the design issues of melody style mining and classification consist of the feature extraction, frequent pattern mining and melody style classification. We extracted the chord from the melody based on the harmony and investigated the representation of extracted features. For each extracted feature, the corresponding frequent pattern mining techniques are developed. For the melody style classification algorithm, we propose the Single-Type Uniform-Support classification (STUS) algorithm which is modified from the associated classification algorithm.
To improve the performance of classification, we propose two new classification algorithms - Single-Type Variant-Support Classification (STVS) and Multi-Type Variant- Support classification (MTVS) algorithm. STVS learns the appropriate minimum supports of every category’s frequent patterns automatically. MTVS algorithm considers all types of frequent patterns for every category further and can decide the appropriate combination of frequent patterns and the corresponding minimum supports.
In the second part, we present a personalized content-based music filtering system to support music recommendation based on user’s preference of melody style. The system learns the user preference by mining the melody patterns from the music access behavior of the user. A two-way melody preference classifier is therefore constructed for each user. Music recommendation is made through this melody preference classifier. Performance evaluation showed that the filtering effect of the proposed approach meets user’s preference.
A new approach for CBMR by the semantic property of music – melody style is proposed in the third part of this thesis. Most CBMR systems provide users the capability to look for music that has been heard. However, sometimes, listeners are looking, not for something they already know, but for something new. Moreover, people sometimes wish to retrieve music that “feels like” another music object or a music style. We propose four types of query specification for melody style query. The output of the melody style query is a music list ranked by the degree of relevance to the query. We adopted melody style mining and classification rule learning algorithm to obtain style classification rules. The style ranking is determined by the style classification rules.
In this thesis, we also investigate music clustering techniques which are useful to browse large music archives. We propose the similarity measures for the representation of the extracted chord-sets and compared the performance of different clustering algorithms with various extracted features. |
| Reference: | [1] Agrawal, R. & Srikant, R. (1994). Fast Algorithms for Mining Association Rules. Proc. of International Conference on Very Large Data Bases VLDB’94.
[2] Agrawal, R. & Srikant, R. (1995). Mining Sequential Patterns. Proc. of International Conference on Data Engineering ICDE’95.
[3] Basu, C., Hirsh, H. & Cohen, W. (1998). Recommendation as Classification: Using Social and Content-Based Information in Recommendation. Proc. of National Conference on Artificial Intelligence AAAI’98.
[4] Billsus, D. & Pazzani, M. (1999). A Hybrid User Model for News Story Classification. Proc. of International Conference on User Modeling.
[5] Chai, W. & Vercoe, B. (2000). Using User Models in Music Information Retrieval Systems. Proc. of International Symposium on Music Information Retrieval ISMIR’00.
[6] Chai, W. & Vercoe, B. (2001). Folk Music Classification Using Hidden Markov Models. Proc. of the International Conference on Artificial Intelligence IC-AI’01.
[7] Chen, H. C. & Chen, A. L. P. (2001). A Music Recommendation System Based on Music Data Grouping and User Interests. Proc. of ACM International Conference on Information and Knowledge Management CIKM’01.
[8] Chen, A. L. P., Chang, Maggie, Chen, Jesse, Hsu, J. L. , Hsu, C. H. and Hua, Spot Y.S. (2000). Query by Music Segments: An Efficient Approach for Song Retrieval. Proc. of IEEE International Conference on Multimedia and Expo ICME’00.
[9] Dannenberg, R. B., Thom, B. & Watson, D. (1997). A Machine Learning Approach to Musical Style Recognition. Proc. of International Computer Music Conference ICMC’97.
[10] Faloutsos, C. & Lin, K. I. (1995). FastMap: a Fast Algorithm for Indexing, Data-Mining and Visualization of Traditional and Multimedia Datasets. Proc. of ACM SIGMOD’95.
[11] Ghias, A., Logan, J., Chamberlin, D. & Smith, B. C. (1995). Query by Humming Musical Information Retrieval in an Audio Database. Proc. of ACM International Multimedia Conference MM’95.
[12] Hsu, J. L., Liu, Chen, A. L. P., Chen, H. C. and Liu, N. H. (2002). The Effectiveness Study of Various Music Information Retrieval Approaches. Proc. of ACM International Conference on Information and Knowledge Management CIKM’02.
[13] Hsu, J. L., Liu, C. C. & Chen, A. L. P. (2001). Discovering Nontrivial Repeating Patterns in Music Data. IEEE Transactions on Multimedia, Vol. 3, No. 3.
[14] Jain, A. K. & Dubes, R. C. (1988). Algorithms for Clustering Data. Prentice-Hall, Inc.
[15] Jain, A. K., Murty, M. N. & Flynn, P. J. (1999). Data Clustering: A Review. ACM Computing Surveys, Vol. 31.
[16] Jang, J. S. R. and Gao, M. Y. (2000). A Query-by-Singing System based on Dynamic Programming. Proc. of International Workshop on Intelligent Systems Resolutions.
[17] Jang, J. S. R. and Lee, H. R. (2001). Hierarchical Filtering Method for Content-based Music Retrieval via Acoustic Input. Proc. of ACM Multimedia MM’01.
[18] Jang, J. S. R., Lee, H. R. & Yeh, C. H. (2001). Query By Tapping: A New Paradigm for Content-Based Music Retrieval from Acoustic Input. Proc. of IEEE Pacific-Rim Conference on Multimedia PCM’01.
[19] Kohonen, T. (1982). Self-organized Formation of Topologically Correct Feature Maps. Biological Cybernetics, Vol. 43.
[20] Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gordon, L. R. & Riedl, J. (1997). GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM, Vol. 40, No. 3.
[21] Kosugi, N., Nishihara, Y., Sakata, T., Yamamuro, M. & Kushima, K. (2000). A Practical Query-By-Humming System for a Large Music Database. Proc. of ACM International Multimedia Conference MM’00.
[22] Kuo, F. F. & Shan, M. K. (2002). A Personalized Music Filtering System Based on Melody Style Classification. Proc. of IEEE International Conference on Data Mining ICDM’02.
[23] Lang, K. (1995). NewsWeeder: Learning to Filter News. Proc. of International Conference on Machine Learning.
[24] Liu, B., Hsu, W. & Ma, Y. (1998). Integrating Classification and Association Rule Mining. Proc. of ACM International Conference on Knowledge Discovery and Data Mining KDD’98.
[25] Liu, C. C., Hsu, J. L. and Chen, A. L. P. (1999). An Approximate String Matching Algorithm for Content-Based Music Data Retrieval. Proc. of IEEE International Conference on Multimedia Computing and Systems ICMCS’99.
[26] Lu, L., You, H. & Zhang, H. J. (2001). A New Approach to Query by Humming In Music Retrieval. Proc. of IEEE International Conference on Multimedia and Expo ICME’01.
[27] Pampalk, E., Rauber, A. & Merkl, D. (2002). Content-based Organization and Visualization of Music Archives. Proc. of ACM International Conference on Multimedia, MM’02.
[28] Rolland, P. Y. (2001). Adaptive User Modeling in a Content-Based Music Retrieval System. Proc. of International Symposium on Music Information Retrieval ISMIR’01.
[29] Rucker, J. & Polanco, M. J. (1997). Personalized Navigation for the Web. Communications of the ACM, Vol. 40, No. 3.
[30] Scholes. Percy A. (1924). The beginner`s guide to harmony. Oxford University Press.
[31] Selfridge-Field, E. (1998). Conceptual and Representational Issues in Melodic Comparison. Melodic Similarity, Concepts, Procedures, and Applications, MIT press.
[32] Shan, M. K., Kuo, F. F. & Chen, M. F. (2002). Music Style Mining and Classification by Melody. Proc. of IEEE International Conference on Multimedia and Expo ICME’02.
[33] Shan, M. K. & Kuo, F. F. (2003). Music Style Mining and Classification by Melody. IEICE Transactions on Information and Systems, Vol. E86-D, No. 4, 2003
[34] Shardanand, U. & Maes, P. (1995). Social Information Filtering: Algorithms for Automating ‘Word of Mouth’. Proc. of the Conference on Human Factors in Computing Systems CHI’95.
[35] Smith, B. & Cotter, P. (2000). A Personalized Television Listings Service. Communications of ACM, Vol. 43, No. 8.
[36] Sonoda, T. & Muraoka,Y. (2000). A WWW-based Melody-Retrieval System: An Indexing Method for A Large Melody Database. Proc. of International Computer Music Conference ICMC’00.
[37] Uitdenbogerd, A. & Zobel, J. (1998). Manipulation of Music for Melody Matching. Proc. of ACM International Multimedia Conference MM’98.
[38] Wang, X., Wang, T. L., Lin, K. I., Shasha, D., Shapiro, B. A. & Zhang, K. (2000). An Index Structure for Data Mining and Clustering, Knowledge and Information Systems, Vol. 2. |
| Description: | 碩士
國立政治大學
資訊科學學系
90753008
91 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0090753008 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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