Title

QueST: Querying Music Databases by Acoustic and Textual Features

Publication Type

Conference Proceeding Article

Publication Date

9-2007

Abstract

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.

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

Proceedings of the 15th ACM International Conference on Multimedia, September 24-29, 2007, Augsburg, Allemagne

First Page

1055

Last Page

1064

ISBN

9781595937025

Identifier

10.1145/1291233.1291465

Publisher

ACM

City or Country

Augsburg, Allemagne

Additional URL

http://dx.doi.org/10.1145/1291233.1291465