Publication Type

Journal Article

Version

acceptedVersion

Publication Date

5-2009

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.

Keywords

Classification, EM algorithm, Evaluation, Gaussian mixture models, Music retrieval, Singer identification, Statistical modeling

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

ACM Transactions on Information Systems

Volume

27

Issue

3

First Page

1

Last Page

31

ISSN

1046-8188

Identifier

10.1145/1508850.1508856

Publisher

ACM

Copyright Owner and License

Authors

Additional URL

http://doi.org/10.1145/1508850.1508856

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