Music Clustering With Features From Different Information Sources

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying ldquosimilarrdquo artists using features from diverse information source...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2009-04, Vol.11 (3), p.477-485
Main Authors: Tao Li, Ogihara, M., Wei Peng, Bo Shao, Shenghuo Zhu
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Applied sciences
Artificial intelligence
Artists
Clustering
Clustering algorithms
Computer science
Computer science
control theory
systems
different information sources
Exact sciences and technology
Fundamental areas of phenomenology (including applications)
Information retrieval
Information sources
Information systems. Data bases
Intelligent structures
Internet
Machine learning
Machine learning algorithms
Memory organisation. Data processing
Multimedia
Music
Music and musical intruments
Music information retrieval
Navigation
Physics
Semisupervised learning
Similarity
Software
Testing
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Summary:Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying ldquosimilarrdquo artists using features from diverse information sources. In this paper, we first present a clustering algorithm that integrates features from both sources to perform bimodal learning. We then present an approach based on the generalized constraint clustering algorithm by incorporating the instance-level constraints. The algorithms are tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity identification can be significantly improved.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2009.2012942