Exploring Composite Acoustic Features for Efficient Music Similarity Query
Conference Proceeding Article
Music similarity query based on acoustic content is becoming important with the ever-increasing growth of the music information from emerging applications such as digital libraries and WWW. However, relative techniques are still in their infancy and much less than satisfactory. In this paper, we present a novel index structure, called Composite Feature tree, CF-tree, to facilitate efficient content-based music search adopting multiple musical features. Before constructing the tree structure, we use PCA to transform the extracted features into a new space sorted by the importance of acoustic features. The CF-tree is a balanced multi-way tree structure where each level represents the data space at different dimensionalities. The PCA transformed data and reduced dimensions in the upper levels can alleviate suffering from dimensionality curse. To accurately mimic human perception, an extension, named CF+-tree, is proposed, which further applies multivariable regression to determine the weight of each individual feature. We conduct extensive experiments to evaluate the proposed structures against state-of-art techniques. The experimental results demonstrate superiority of our technique.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
MULTIMEDIA '06: Proceedings of the 14th ACM International Conference on Multimedia, October 23-27, Santa Barbara, CA
City or Country
Santa Barbara, CA
CUI, Bin; SHEN, Jialie; CONG, Gao; SHEN, Heng Tao; and YU, Cui.
Exploring Composite Acoustic Features for Efficient Music Similarity Query. (2006). MULTIMEDIA '06: Proceedings of the 14th ACM International Conference on Multimedia, October 23-27, Santa Barbara, CA. 412-420. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1230