QueST: Querying Music Databases by Acoustic and Textual Features
Conference Proceeding Article
With continued growth of music content available on the Internet, music information retrieval has attracted increasing attention. An important challenge for music searching is its ability to support both keyword and content based queries efficiently and with high precision. In this paper, we present a music query system - QueST (Query by acouStic and Textual features) to support both keyword and content based retrieval in large music databases. QueST has two distinct features. First, it provides new index schemes that can efficiently handle various queries within a uniform architecture. Concretely, we propose a hybrid structure consisting of Inverted file and Signature file to support keyword search. For content based query, we introduce the notion of similarity to capture various music semantics like melody and genre. We extract acoustic features from a music object, and map it to multiple high-dimension spaces with respect to the similarity notion using PCA and RBF neural network. Second, we design a result fusion scheme, called the Quick Threshold Algorithm, to speed up the processing of complex queries involving both textual and multiple acoustic features. Our experimental results show that QueST offers higher accuracy and efficiency compared to existing algorithms.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 15th ACM International Conference on Multimedia, September 24-29, 2007, Augsburg, Allemagne
City or Country
CUI, Bin; LIU, Ling; Pu, Calton; SHEN, Jialie; and TAN, Kian-Lee.
QueST: Querying Music Databases by Acoustic and Textual Features. (2007). Proceedings of the 15th ACM International Conference on Multimedia, September 24-29, 2007, Augsburg, Allemagne. 1055-1064. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/291