Music Clustering With Features From Different Information Sources

• Published in: IEEE transactions on multimedia Vol. 11; no. 3; pp. 477 - 485
• Main Authors: Tao Li, Ogihara, M., Wei Peng, Bo Shao, Shenghuo Zhu
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01-04-2009; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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; Cluster analysis; different information sources; Similarity; Information system; Information retrieval; Similarity solution; Experimental study; machine learning; Song; Information source; Music; Musical acoustics; Classification; clustering; Artificial intelligence; music information retrieval
• ISSN: 1520-9210; 1941-0077
• DOI: 10.1109/TMM.2009.2012942

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.