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

作者：Shen Jialie^*; Shepherd John; Cui Bin; Tan Kian Lee

来源：ACM Transactions on Information Systems, 2009, 27(3): 18.

DOI：10.1145/1508850.1508856

• Algorithms
• Experimentation
• Performance
• Music retrieval
• singer identification
• Gaussian mixture models
• classification
• EM algorithm
• statistical modeling
• evaluation

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.

• 出版日期2009
• 单位北京大学