A novel framework for efficient automated singer identification in large music databases

  • Authors:

  • Jialie Shen;John Shepherd;Bin Cui;Kian-Lee Tan

  • Affiliations:

  • Singapore Management University, Singapore;The University of New South Wales, Sidney, Australia;Peking University, Beijing, China;National University of Singapore, Kent Ridge, Singapore

  • Venue:
  • ACM Transactions on Information Systems (TOIS)
  • Year:
  • 2009

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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.