Abstract
Unsupervised clustering algorithms can be combined to improve the robustness and the quality of the results, e.g. in blind source separation. Before combining the results of these clustering methods the corresponding clusters have to be aligned, but usually it is not known which clusters of the employed methods correspond to each other. In this paper, we present a method to avoid this correspondence problem using probability theory. We also present an application of our method in blind source separation. Our approach is better expandable than other state-of-the-art separation algorithms while leading to slightly better results.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Strehl, A., Ghosh, J.: Cluster ensembles — a knowledge reuse framework for combining multiple partitions. J. Mach. Learn. Res. 3, 583–617 (2003)
Boulis, C., Ostendorf, M.: Combining multiple clusterings using evidence accumulation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27 (2005)
Boulis, C., Ostendorf, M.: Combining Multiple Clustering Systems. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) PKDD 2004. LNCS (LNAI), vol. 3202, pp. 63–74. Springer, Heidelberg (2004)
FitzGerald, D., Cranitch, M., Coyle, E.: Extended nonnegative tensor factorisation models for musical sound source separation. In: Computational Intelligence and Neuroscience (2008)
Ozerov, A., Févotte, C.: Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. IEEE Transactions on Audio, Speech, and Language Processing 18(3), 550–563 (2010), http://www.irisa.fr/metiss/ozerov/demos.html
Spiertz, M., Gnann, V.: Source-filter based clustering for monaural blind source separation. In: Proc. of International Conference on Digital Audio Effects DAFx, Como, Italy (2009)
Spiertz, M., Gnann, V.: Note clustering based on 2d source-filter modeling for underdetermined blind source separation. In: Proceedings of the AES 42nd International Conference on Semantic Audio, Ilmenau, Germany (July 2011)
Vincent, E., Gribonval, R., Fevotte, C.: Performance measurement in blind audio source separation. IEEE Transactions on Audio, Speech, and Language Processing 14(4), 1462–1469 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Becker, J.M., Spiertz, M., Gnann, V. (2012). A Probability-Based Combination Method for Unsupervised Clustering with Application to Blind Source Separation. In: Theis, F., Cichocki, A., Yeredor, A., Zibulevsky, M. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2012. Lecture Notes in Computer Science, vol 7191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28551-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-642-28551-6_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28550-9
Online ISBN: 978-3-642-28551-6
eBook Packages: Computer ScienceComputer Science (R0)