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 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.

[1]  S. S. Ravi,et al.  Clustering with Constraints: Feasibility Issues and the k-Means Algorithm , 2005, SDM.

[2]  Ayhan Demiriz,et al.  Semi-Supervised Clustering Using Genetic Algorithms , 1999 .

[3]  Yan Zhou,et al.  Enhancing Supervised Learning with Unlabeled Data , 2000, ICML.

[4]  Avrim Blum,et al.  The Bottleneck , 2021, Monopsony Capitalism.

[5]  Sharon L. Oviatt,et al.  Multimodal Integration - A Statistical View , 1999, IEEE Trans. Multim..

[6]  Raymond J. Mooney,et al.  Integrating constraints and metric learning in semi-supervised clustering , 2004, ICML.

[7]  Tao Li,et al.  A comparative study on content-based music genre classification , 2003, SIGIR.

[8]  G. W. Milligan,et al.  A Study of the Comparability of External Criteria for Hierarchical Cluster Analysis. , 1986, Multivariate behavioral research.

[9]  Rayid Ghani,et al.  Analyzing the effectiveness and applicability of co-training , 2000, CIKM '00.

[10]  Roger Mitton,et al.  Spelling checkers, spelling correctors and the misspellings of poor spellers , 1987, Inf. Process. Manag..

[11]  Joydeep Ghosh,et al.  Cluster Ensembles --- A Knowledge Reuse Framework for Combining Multiple Partitions , 2002, J. Mach. Learn. Res..

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[13]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[14]  George Karypis,et al.  Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering , 2004, Machine Learning.

[15]  William M. Rand,et al.  Objective Criteria for the Evaluation of Clustering Methods , 1971 .

[16]  Yoram Singer,et al.  Unsupervised Models for Named Entity Classification , 1999, EMNLP.

[17]  Elias Pampalk,et al.  Content-based organization and visualization of music archives , 2002, MULTIMEDIA '02.

[18]  Shenghuo Zhu,et al.  Integrating Features from Different Sources for Music Information Retrieval , 2006, Sixth International Conference on Data Mining (ICDM'06).

[19]  Raymond J. Mooney,et al.  A probabilistic framework for semi-supervised clustering , 2004, KDD.

[20]  Tao Li,et al.  Semisupervised learning from different information sources , 2005, Knowledge and Information Systems.

[21]  Eric Brill,et al.  Some Advances in Transformation-Based Part of Speech Tagging , 1994, AAAI.

[22]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[23]  Aristides Gionis,et al.  Clustering Aggregation , 2005, ICDE.

[24]  Dan Roth,et al.  Toward a Theory of Learning Coherent Concepts , 2000, AAAI/IAAI.

[25]  Ronald de Wolf,et al.  Algorithmic Clustering of Music Based on String Compression , 2004, Computer Music Journal.

[26]  Chris H. Q. Ding,et al.  Orthogonal nonnegative matrix t-factorizations for clustering , 2006, KDD '06.

[27]  Sanjoy Dasgupta,et al.  PAC Generalization Bounds for Co-training , 2001, NIPS.

[28]  Sethuraman Panchanathan,et al.  Wavelet-histogram method for face recognition , 2000, J. Electronic Imaging.

[29]  Hsin-Min Wang,et al.  Blind Clustering of Popular Music Recordings Based on Singer Voice Characteristics , 2004, Computer Music Journal.

[30]  Jill P. Mesirov,et al.  Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.

[31]  George Tzanetakis,et al.  Musical genre classification of audio signals , 2002, IEEE Trans. Speech Audio Process..

[32]  Arindam Banerjee,et al.  Semi-supervised Clustering by Seeding , 2002, ICML.

[33]  Dana H. Ballard,et al.  Category Learning Through Multimodality Sensing , 1998, Neural Computation.

[34]  Wei Peng,et al.  Music Clustering with Constraints , 2007, ISMIR.

[35]  Suzanna Becker,et al.  Mutual information maximization: models of cortical self-organization. , 1996, Network.