Publication statistics

Pub. period:1996-2010
Pub. count:8
Number of co-authors:9



Co-authors

Number of publications with 3 favourite co-authors:

Jialie Shen:4
Kian-Lee Tan:2
Bin Cui:2

 

 

Productive colleagues

John Shepherd's 3 most productive colleagues in number of publications:

Narciso Cerpa:69
Kian-Lee Tan:13
Jialie Shen:12
 
 
 

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John Shepherd

 

Publications by John Shepherd (bibliography)

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2010
 
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Tam, Vincent W. L. and Shepherd, John (2010): Webpage relationships for information retrieval within a structured domain. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermedia 2010. pp. 307-308. Available online

In this paper, we describe a technique for improving the effectiveness of information retrieval within individual websites. Our proposed approach exploits the link structures within the site and the idea that relevant content maybe spread across several related pages within the site. We present experiments which compare our approach to other web search techniques.

© All rights reserved Tam and Shepherd and/or their publisher

2009
 
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Shen, Jialie, Shepherd, John, Cui, Bin and Tan, Kian-Lee (2009): A novel framework for efficient automated singer identification in large music databases. In ACM Transactions on Information Systems, 27 (3) p. 18. Available online

Over the past decade, there has been explosive growth in the availability of multimedia data, particularly image, video, and music. Because of this, content-based music retrieval has attracted attention from the multimedia database and information retrieval communities. Content-based music retrieval requires us to be able to automatically identify particular characteristics of music data. One such characteristic, useful in a range of applications, is the identification of the singer in a musical piece. Unfortunately, existing approaches to this problem suffer from either low accuracy or poor scalability. In this article, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for efficient automated singer recognition. HSI uses multiple low-level features extracted from both vocal and nonvocal music segments to enhance the identification process; it achieves this via a hybrid architecture that builds profiles of individual singer characteristics based on statistical mixture models. An extensive experimental study on a large music database demonstrates the superiority of our method over state-of-the-art approaches in terms of effectiveness, efficiency, scalability, and robustness.

© All rights reserved Shen et al. and/or ACM Press

2006
 
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Shen, Jialie, Cui, Bin, Shepherd, John and Tan, Kian-Lee (2006): Towards efficient automated singer identification in large music databases. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006. pp. 59-66. Available online

Automated singer identification is important in organising, browsing and retrieving data in large music databases. In this paper, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for automated singer recognition. HSI can effectively use multiple low-level features extracted from both vocal and non-vocal music segments to enhance the identification process with a hybrid architecture and build profiles of individual singer characteristics based on statistical mixture models. Extensive experimental results conducted on a large music database demonstrate the superiority of our method over state-of-the-art approaches.

© All rights reserved Shen et al. and/or ACM Press

 
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Shen, Jialie and Shepherd, John (2006): Efficient benchmarking of content-based image retrieval via resampling. In: Nahrstedt, Klara, Turk, Matthew, Rui, Yong, Klas, Wolfgang and Mayer-Patel, Ketan (eds.) Proceedings of the 14th ACM International Conference on Multimedia October 23-27, 2006, Santa Barbara, CA, USA. pp. 569-578. Available online

2004
 
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Ma, Liping and Shepherd, John (2004): Information extraction using two-phase pattern discovery. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2004. pp. 534-535. Available online

This paper presents a new two-phase pattern (2PP) discovery technique for information extraction. 2PP consists of orthographic pattern discovery (OPD) and semantic pattern discovery (SPD) where the OPD determines the structural features from an identified region of a document and the SPD discovers a dominant semantic pattern for the region via inference, apposition and analogy. Then the discovered pattern is applied back into the region to extract required data items through pattern matching. We evaluated 2PP using 6500 data items and obtained effective result.

© All rights reserved Ma and Shepherd and/or ACM Press

 
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Shen, Jialie, Shepherd, John and Ngu, Anne H. H. (2004): Integrating heterogeneous reatures for efficient content based music retrieval. In: Grossman, David A., Gravano, Luis, Zhai, Chengxiang, Herzog, Otthein and Evans, David A. (eds.) Proceedings of the 2004 ACM CIKM International Conference on Information and Knowledge Management November 8-13, 2004, Washington, DC, USA. pp. 154-155. Available online

1998
 
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Kurniawati, Ruth, Jin, Jesse S. and Shepherd, John (1998): An Efficient Nearest-Neighbour Search While Varying Euclidean Metrics. In: ACM Multimedia 1998 1998. pp. 411-418. Available online

1996
 
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Cerpa, Narciso and Shepherd, John (1996): A Cognitive Model for Facilitating the Teaching of Computer Programming Skills. In: Proceedings of the First Australasian Conference on Computer Science Education July, 1996, Sydney, Australia. pp. 1-6.

 
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