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Title: A novel framework for efficient automated singer identification in large music databases
Authors: Shen, J.
Shepherd, J.
Cui, B.
Tan, K.-L. 
Keywords: Classification
EM algorithm
Gaussian mixture models
Music retrieval
Singer identification
Statistical modeling
Issue Date: 2009
Citation: Shen, J., Shepherd, J., Cui, B., Tan, K.-L. (2009). A novel framework for efficient automated singer identification in large music databases. ACM Transactions on Information Systems 27 (3). ScholarBank@NUS Repository.
Abstract: 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 ACM.
Source Title: ACM Transactions on Information Systems
ISSN: 10468188
DOI: 10.1145/1508850.1508856
Appears in Collections:Staff Publications

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