A Novel Framework for Efficient Automated Singer Identification in Large Music Databases
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...
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2009
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sg-smu-ink.sis_research-17782017-03-23T06:22:56Z A Novel Framework for Efficient Automated Singer Identification in Large Music Databases SHEN, Jialie Shepherd, John CUI, Bin TAN, Kian-Lee 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. 2009-05-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/779 info:doi/10.1145/1508850.1508856 https://ink.library.smu.edu.sg/context/sis_research/article/1778/viewcontent/NovelFrameworkEffAutoSingerId_18_shen.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Classification EM algorithm Evaluation Gaussian mixture models Music retrieval Singer identification Statistical modeling Databases and Information Systems Numerical Analysis and Scientific Computing |
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Classification EM algorithm Evaluation Gaussian mixture models Music retrieval Singer identification Statistical modeling Databases and Information Systems Numerical Analysis and Scientific Computing SHEN, Jialie Shepherd, John CUI, Bin TAN, Kian-Lee A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
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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. |
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SHEN, Jialie Shepherd, John CUI, Bin TAN, Kian-Lee |
author_facet |
SHEN, Jialie Shepherd, John CUI, Bin TAN, Kian-Lee |
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SHEN, Jialie |
title |
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
title_short |
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
title_full |
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
title_fullStr |
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
title_full_unstemmed |
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases |
title_sort |
novel framework for efficient automated singer identification in large music databases |
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Institutional Knowledge at Singapore Management University |
publishDate |
2009 |
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https://ink.library.smu.edu.sg/sis_research/779 https://ink.library.smu.edu.sg/context/sis_research/article/1778/viewcontent/NovelFrameworkEffAutoSingerId_18_shen.pdf |
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1770570710155001856 |