Browsing by Author "Shan, Man-Kwan"

Now showing 1 - 12 of 12

Automatic System for the Arrangement of Piano Reductions
(2009)
Chiu, Shih-Chuan
;
Shan, Man-Kwan
;
Huang, Jiun-Long
;
資訊工程學系
;
Department of Computer Science
Piano reduction is a process that arranges music for the piano by reducing the original music into the most basic components. In this study we present an automatic arrangement system for piano reduction that arranges music algorithmically for the piano while considering various roles of the piano in music. We achieve this by first analyzing the original music in order to determine the type of arrangement element performed by an instrument. Then each phrase is identified and is associated with a weighted importance value. At last, a phrase selection algorithm is proposed to select phrases with maximum importance to arrangement under the constraint of piano playability. Our experiments demonstrate that the proposed system has the ability to create piano arrangement.
Computer music composition based on discovered music patterns
(2006)
Chiu, Shih-Chuan
;
Shan, Man-Kwan
;
資訊工程學系
;
Department of Computer Science
Computer music composition has been the dream of the computer music researcher. In this paper, we investigated the approach to discover the rules of music composition from given music objects, and automatically generate a new music object style similar to the given music objects. The proposed approach utilizes the data mining techniques to discover the rules of music composition characterized by the music properties, music structure, melody style and motif. A new music object is generated based on the discovered rules. To measure the effectiveness of proposed computer music composition approach, we adopted the method similar to the Turing test to test the discrimination between machine-generated and human-composed music. Experimental results showed that it is hard to discriminate. Another experiment showed that the style of generated music is similar to the given music objects.
Detecting changes in user-centered Music Query Streams
(2006)
Li, Hua-Fu
;
Shan, Man-Kwan
;
Lee, Suh-Yin
;
資訊工程學系
;
Department of Computer Science
In this paper, we propose an efficient algorithm, called MQS-change (changes of Music Query. Streams), to detect the changes of maximal melody structures in user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new data structure MSC-list (a list of Music Structure Changes). Experiments show that MQS-change algorithm is an online, single-pass approach to detect the changes of music melody structures over continuous music query streams.
DSM-FI: an efficient algorithm for mining frequent itemsets in data streams
(2008-10-01)
Li, Hua-Fu
;
Shan, Man-Kwan
;
Lee, Suh-Yin
;
資訊工程學系
;
Department of Computer Science
Online mining of data streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some inherent characteristics. In this paper, we propose a new single-pass algorithm, called DSM-FI (data stream mining for frequent itemsets), for online incremental mining of frequent itemsets over a continuous stream of online transactions. According to the proposed algorithm, each transaction of the stream is projected into a set of sub-transactions, and these sub-transactions are inserted into a new in-memory summary data structure, called SFI-forest (summary frequent itemset forest) for maintaining the set of all frequent itemsets embedded in the transaction data stream generated so far. Finally, the set of all frequent itemsets is determined from the current SFI-forest. Theoretical analysis and experimental studies show that the proposed DSM-FI algorithm uses stable memory, makes only one pass over an online transactional data stream, and outperforms the existing algorithms of one-pass mining of frequent itemsets.
DSM-PLW: Single-pass mining of path traversal patterns over streaming Web click-sequences
(2006-07-14)
Li, Hua-Fu
;
Lee, Suh-Yin
;
Shan, Man-Kwan
;
資訊工程學系
;
Department of Computer Science
Mining Web click streams is an important data mining problem with broad applications. However, it is also a difficult problem since the streaming data possess some interesting characteristics, such as unknown or unbounded length, possibly a very fast arrival rate, inability to backtrack over previously arrived click-sequences, and a lack of system control over the order in which the data arrive. In this paper, we propose a projection-based, single-pass algorithm, called DSM-PLW (Data Stream Mining for Path traversal patterns in a Landmark Window), for online incremental mining of path traversal patterns over a continuous stream of maximal forward references generated at a rapid rate. According to the algorithm, each maximal forward reference of the stream is projected into a set of reference-suffix maximal forward references, and these reference-suffix maximal forward references are inserted into a new in-memory summary data structure, called SP-forest (Summary Path traversal pattern forest), which is an extended prefix tree-based data structure for storing essential information about frequent reference sequences of the stream so far. The set of all maximal reference sequences is determined from the SP-forest by a depth-first-search mechanism, called MRS-mining (Maximal Reference Sequence mining). Theoretical analysis and experimental studies show that the proposed algorithm has gently growing memory requirements and makes only one pass over the streaming data. (c) 2005 Elsevier B.V. All rights reserved.
Emotion-based music recommendation by affinity discovery from film music
(2009-05-01)
Shan, Man-Kwan
;
Kuo, Fang-Fei
;
Chiang, Meng-Fen
;
Lee, Suh-Yin
;
資訊工程學系
With the growth of digital music, the development of music recommendation is helpful for users to pick desirable music pieces from a huge repository of music. The existing music recommendation approaches are based on a user's preference on music. However, sometimes, it might better meet users' requirement to recommend music pieces according to emotions. In this paper, we propose a novel framework for emotion-based music recommendation. The core of the recommendation framework is the construction of the music emotion model by affinity discovery from film music, which plays an important role in conveying emotions in film. We investigate the music feature extraction and propose the Music Affinity Graph and Music Affinity Graph-Plus algorithms for the construction of music emotion model. Experimental result shows the proposed emotion-based music recommendation achieves 85% accuracy in average. (C) 2008 Elsevier Ltd. All rights reserved.
Exploring heterogeneous information networks and random walk with restart for academic search
(2013-07-01)
Chiang, Meng-Fen
;
Liou, Jiun-Jiue
;
Wang, Jen-Liang
;
Peng, Wen-Chih
;
Shan, Man-Kwan
In this paper, we explore heterogenous information networks in which each vertex represents one entity and the edges reflect linkage relationships. Heterogenous information networks contain vertices of several entity types, such as papers, authors and terms, and hence can fully reflect multiple linkage relationships among different entities. Such a heterogeneous information network is similar to a mixed media graph (MMG). By representing a bibliographic dataset as an MMG, the performance obtained when searching relevant entities (e.g., papers) can be improved. Furthermore, our academic search enables multiple-entity search, where a variety of entity search results are provided, such as relevant papers, authors and conferences, via a one-time query. Explicitly, given a bibliographic dataset, we propose a Global-MMG, in which a global heterogeneous information network is built. When a user submits a query keyword, we perform a random walk with restart (RWR) to retrieve papers or other types of entity objects. To reduce the query response time, algorithm Net-MMG (standing for NetClus-based MMG) is developed. Algorithm Net-MMG first divides a heterogeneous information network into a collection of sub-networks. Afterward, the Net-MMG performs a RWR on a set of selected relevant sub-networks. We implemented our academic search and conducted extensive experiments using the ACM Digital Library. The experimental results show that by exploring heterogeneous information networks and RWR, both the Global-MMG and Net-MMG achieve better search quality compared with existing academic search services. In addition, the Net-MMG has a shorter query response time while still guaranteeing good quality in search results.
MINING POLYPHONIC REPEATING PATTERNS FROM MUSIC DATA USING BIT-STRING BASED APPROACHES
(2009)
Chiu, Shih-Chuan
;
Shan, Man-Kwan
;
Huang, Jiun-Long
;
Li, Hua-Fu
;
資訊工程學系
Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (priori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from music data. Furthermore, a bit-string method is developed for improving the efficiency of the proposed algorithms. Experimental results show that the proposed algorithms, A-PRPD and T-PRPD, are both effective and efficient methods for mining polyphonic repeating patterns from synthetic music data and real data.
Mining temporal co-orientation pattern from spatio-temporal databases
(2007)
Wei, Ling-Yin
;
Shan, Man-Kwan
;
資訊工程學系
;
Department of Computer Science
A spatial co-orientation pattern refers to objects that frequently occur with the same spatial orientation, e.g. left, right, below, etc., among images. In this paper, we introduce temporal co-orientation pattern mining which is the problem of temporal aspects of spatial co-orientation patterns. A temporal co-orientation pattern represents how spatial co-orientation patterns change over time. Temporal co-orientation pattern mining is useful for discovering tactics from play sequences of sports video data, because the most tactic patterns of basketball competition are constituted of such spatial co-orientation patterns in time order. We propose the three-stage approach, which transforms the problem into sequential pattern mining, for mining temporal co-orientation patterns. We experimentally evaluate the performance of the proposed algorithm and analysis the effect of these stages.
Online mining of recent music query streams
(2006)
Li, Hua-Fu
;
Ho, Chin-Chuan
;
Shan, Man-Kwan
;
Lee, Suh-Yin
;
資訊工程學系
Mining multimedia data is one of the most important issues in data mining. In this paper, we propose an online one-pass algorithm to mine the set of frequent temporal patterns in online music query streams with a sliding window. An effective bit-sequence representation is used to reduce the processing time and memory needed to slide the windows. Experiments show that the proposed algorithm only needs a half of memory requirement of original music query data, and just scans the data once.
Relevance feedback for category search in music retrieval based on semantic concept learning
(2008-09-01)
Shan, Man-Kwan
;
Chiang, Meng-Fen
;
Kuo, Fang-Fei
;
資訊工程學系
;
Department of Computer Science
Traditional content-based music retrieval systems retrieve a specific music object which is similar to what a user has requested. However, the need exists for the development of category search for the retrieval of a specific category of music objects which share a common semantic concept. The concept of category search in content-based music retrieval is subjective and dynamic. Therefore, this paper investigates a relevance feedback mechanism for category search of polyphonic symbolic music based on semantic concept learning. For the consideration of both global and local properties of music objects, a segment-based music object modeling approach is presented. Furthermore, in order to discover the user semantic concept in terms of discriminative features of discriminative segments, a concept learning mechanism based on data mining techniques is proposed to find the discriminative characteristics between relevant and irrelevant objects. Moreover, three strategies, the Most-Positive, the Most-Informative, and the Hybrid, to return music objects concerning user relevance judgments are investigated. Finally, comparative experiments are conducted to evaluate the effectiveness of the proposed relevance feedback mechanism. Experimental results show that, for a database of 215 polyphonic music objects, 60% average precision can be achieved through the use of the proposed relevance feedback mechanism.
Towards an Automatic Music Arrangement Framework Using Score Reduction
(2012-01-01)
Huang, Jiun-Long
;
Chiu, Shih-Chuan
;
Shan, Man-Kwan
;
資訊工程學系
;
Department of Computer Science
Score reduction is a process that arranges music for a target instrument by reducing original music. In this study we present a music arrangement framework that uses score reduction to automatically arrange music for a target instrument. The original music is first analyzed to determine the type of arrangement element of each section, then the phrases are identified and each is assigned a utility according to its type of arrangement element. For a set of utility-assigned phrases, we transform the music arrangement into an optimization problem and propose a phrase selection algorithm. The music is arranged by selecting appropriate phrases satisfying the playability constraints of a target instrument. Using the proposed framework, we implement a music arrangement system for the piano. An approach similar to Turing test is used to evaluate the quality of the music arranged by our system. The experiment results show that our system is able to create viable music for the piano.