Towards efficient automated singer identification in large music databases
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
A novel framework for efficient automated singer identification in large music databases
ACM Transactions on Information Systems (TOIS)
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The singer's information is essential in organising, browsing and exploring music data. As an important component of music database systems, the automated artist identification is gaining considerable momentum due to numerous potential applications including music indexing and retrieval, copy right management and music recommendation systems. Unfortunately, the most currently employed approaches are still in their infancy and the performance is by far less satisfactory. Indeed, they suffer from low effectiveness, less robustness and poor scalability to accommodate large scale of data. In this demo, we presents a novel system, called Hybrid Singer Identifier (HSI), for efficient and effective automated singer identification in large music databases.