Towards Efficient Automated Singer Identification in Large Music Databases
Conference Proceeding Article
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.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval
SHEN, Jialie; Bin, Cui; John, Shepherd; and Kian-Lee, TAN.
Towards Efficient Automated Singer Identification in Large Music Databases. (2006). SIGIR '06: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval. 59-66. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1231