Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/41789
Title: Towards efficient automated singer identification in large music databases
Authors: Shen, J.
Cui, B.
Shepherd, J.
Tan, K.-L. 
Keywords: Music Retrieval
Singer Identification
Issue Date: 2006
Source: Shen, J.,Cui, B.,Shepherd, J.,Tan, K.-L. (2006). Towards efficient automated singer identification in large music databases. Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 2006 : 59-66. ScholarBank@NUS Repository.
Abstract: Automated singer identification is important in organising, browsing and retrieving data in large music databases. In this paper, we propose a novel scheme, called Hybrid Singer Identifier (HSI), for automated singer recognition. HSI can effectively use multiple low-level features extracted from both vocal and non-vocal music segments to enhance the identification process with a hybrid architecture and build profiles of individual singer characteristics based on statistical mixture models. Extensive experimental results conducted on a large music database demonstrate the superiority of our method over state-of-the-art approaches. Copyright 2006 ACM.
Source Title: Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
URI: http://scholarbank.nus.edu.sg/handle/10635/41789
ISBN: 1595933697
Appears in Collections:Staff Publications

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