A Novel Framework for Efficient Automated Singer Identification in Large Music Databases

• Main Authors: SHEN, Jialie, Shepherd, John, CUI, Bin, TAN, Kian-Lee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2009
• Subjects: Classification; EM algorithm; Evaluation; Gaussian mixture models; Music retrieval; Singer identification; Statistical modeling; Databases and Information Systems; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/779; https://ink.library.smu.edu.sg/context/sis_research/article/1778/viewcontent/NovelFrameworkEffAutoSingerId_18_shen.pdf
• Institution: Singapore Management University

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.