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.
Classification, EM algorithm, Evaluation, Gaussian mixture models, Music retrieval, Singer identification, Statistical modeling
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
ACM Transactions on Information Systems
SHEN, Jialie; Shepherd, John; CUI, Bin; and TAN, Kian-Lee.
A Novel Framework for Efficient Automated Singer Identification in Large Music Databases. (2009). ACM Transactions on Information Systems. 27, (3), 1-31. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/779
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Creative Commons License
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