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|Title:||A novel framework for efficient automated singer identification in large music databases||Authors:||Shen, J.
Gaussian mixture models
|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. https://doi.org/10.1145/1508850.1508856||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||URI:||http://scholarbank.nus.edu.sg/handle/10635/39079||ISSN:||10468188||DOI:||10.1145/1508850.1508856|
|Appears in Collections:||Staff Publications|
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