DocumentCode :
1629618
Title :
HSI: A Novel Framework for Efficient Automated Singer Identification in Large Music Database
Author :
Shen, Jialie ; Shepherd, John ; Cui, Bin ; Tan, Kian-Lee
Author_Institution :
UNSW, Australia
fYear :
2006
Firstpage :
169
Lastpage :
169
Abstract :
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.
Keywords :
Australia; Database systems; Delay; Indexing; Large-scale systems; Multiple signal classification; Music information retrieval; Noise robustness; Recommender systems; Scalability;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on
Print_ISBN :
0-7695-2570-9
Type :
conf
DOI :
10.1109/ICDE.2006.79
Filename :
1617537
Link To Document :
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