Exploring Composite Acoustic Features for Efficient Music Similarity Query

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 pre...

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Main Authors: CUI, Bin, SHEN, Jialie, CONG, Gao, SHEN, Heng Tao, YU, Cui
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Language:English
Published: Institutional Knowledge at Singapore Management University 2006
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Online Access:https://ink.library.smu.edu.sg/sis_research/1230
http://dx.doi.org/10.1145/1180639.1180725
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-22292010-12-22T08:24:06Z Exploring Composite Acoustic Features for Efficient Music Similarity Query CUI, Bin SHEN, Jialie CONG, Gao SHEN, Heng Tao YU, Cui 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. 2006-10-23T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1230 info:doi/10.1145/1180639.1180725 http://dx.doi.org/10.1145/1180639.1180725 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
CUI, Bin
SHEN, Jialie
CONG, Gao
SHEN, Heng Tao
YU, Cui
Exploring Composite Acoustic Features for Efficient Music Similarity Query
description 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.
format text
author CUI, Bin
SHEN, Jialie
CONG, Gao
SHEN, Heng Tao
YU, Cui
author_facet CUI, Bin
SHEN, Jialie
CONG, Gao
SHEN, Heng Tao
YU, Cui
author_sort CUI, Bin
title Exploring Composite Acoustic Features for Efficient Music Similarity Query
title_short Exploring Composite Acoustic Features for Efficient Music Similarity Query
title_full Exploring Composite Acoustic Features for Efficient Music Similarity Query
title_fullStr Exploring Composite Acoustic Features for Efficient Music Similarity Query
title_full_unstemmed Exploring Composite Acoustic Features for Efficient Music Similarity Query
title_sort exploring composite acoustic features for efficient music similarity query
publisher Institutional Knowledge at Singapore Management University
publishDate 2006
url https://ink.library.smu.edu.sg/sis_research/1230
http://dx.doi.org/10.1145/1180639.1180725
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