Music clustering with features from different information sources

Tao Li, Mitsunori Ogihara, Wei Peng, Bo Shao, Shenghuo Zhu

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 similar 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.

Original languageEnglish (US)
Article number4797802
Pages (from-to)477-484
Number of pages8
JournalIEEE Transactions on Multimedia
Issue number3
StatePublished - Apr 2009


  • Clustering
  • Different information sources
  • Machine learning
  • Music information retrieval

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering


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