Abstract
Classification of audio recordings is often based on audio-signal features. The number of available variables is usually very large. For successful categorization in e.g. genres, substyles or personal preferences small, but very predictive feature sets are sought. A further challenge is to solve this feature selection problem at least approximately with short run lengths to reduce the high computational load. We pursue this goal by applying asymmetric mutation operators in simple evolutionary strategies, which are further enhanced by mixing in greedy search operators. The resulting algorithm is reliably better than any of these approaches alone and in most cases clearly better than a deterministic greedy strategy.
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
Beyer, H.-G., Schwefel, H.-P.: Evolution strategies – A comprehensive introduction. Natural Computing: an International Journal 1, 3–52 (2002)
Bischl, B.: The mlr Package: Machine Learning in R (2010), http://mlr.r-forge.r-project.org
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth (1984)
Guyon, I.: An Introduction to Variable and Feature Selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)
Hall, M.A., Holmes, G.: Benchmarking attribute selection techniques for discrete class data mining. IEEE Transactions on Knowledge and Data Engineering 15(6), 1437–1447 (2003)
Jelasity, M., Preuß, M., Eiben, A.E.: Operator Learning for a Problem Class in a Distributed Peer-to-peer Environment. In: Guervós, J.J.M., Adamidis, P.A., Beyer, H.-G., Fernández-Villacañas, J.-L., Schwefel, H.-P. (eds.) PPSN 2002. LNCS, vol. 2439, pp. 172–183. Springer, Heidelberg (2002)
Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artificial Intelligence 97(1-2), 273–324 (1997)
Lartillot, O., Toiviainen, P.: MIR in Matlab (II): A Toolbox for Musical Feature Extraction From Audio. In: Proceedings of the 8th International Conference on Music Information Retrieval (ISMIR), pp. 127–130 (2007)
Loughrey, J., Cunningham, P.: Overfitting in Wrapper-Based Feature Subset Selection: The Harder You Try the Worse it Gets. In: Research and Development in Intelligent Systems XXI, pp. 33–43 (2005)
Loughrey, J., Cunningham, P.: Early-Stopping to Avoid Overfitting in Wrapper-Based Feature Selection Employing Stochastic Search. Computer Science Technical Report, Trinity College Dublin, TCD-CS-2005-37 (2005)
Pohle, T., Pampalk, E., Widmer, G.: Evaluation of Frequently Used Audio Features for Classification of Music into Perceptual Categories. In: Fourth International Workshop on Content-Based Multimedia Indexing (2005)
Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognition 39(12), 2383–2392 (2006)
Schwefel, H.-P.: Cybernetic Evolution as Strategy for Experimental Research in Fluid Mechanics. Diploma Thesis, Hermann Föttinger-Institute for Fluid Mechanics, Technical University of Berlin (1965) (in German)
Theimer, W., Vatolkin, I., Eronen, A.: Definitions of Audio Features for Music Content Description, Algorithm Engineering Report TR08-2-001, Technical University Dortmund (2008)
Vatolkin, I., Theimer, W.: Optimization of Feature Processing Chain in Music Classification by Evolution Strategies. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1150–1159. Springer, Heidelberg (2008)
Vatolkin, I., Theimer, W., Rudolph, G.: Design and Comparison of Different Evolution Strategies for Feature Selection and Consolidation in Music Classification. In: Proceedings of the 2009 IEEE Congress on Evolutionary Computation (CEC 2009). IEEE Press, Piscataway (2009)
R Development Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2010), ISBN 3-900051-07-0, http://www.R-project.org
Rechenberg, I.: Cybernetic Solution Path of an Experimental Problem. In: Fogel, D.B. (ed.) Evolutionary Computation - The Fossil Record. IEEE Press, Los Alamitos (1998)
Greenwood, G., Zhu, Q.: Convergence in Evolutionary Programs with Self-Adaptation. Evolutionary Computation 9(2), 147–158 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bischl, B., Vatolkin, I., Preuss, M. (2010). Selecting Small Audio Feature Sets in Music Classification by Means of Asymmetric Mutation. In: Schaefer, R., Cotta, C., Kołodziej, J., Rudolph, G. (eds) Parallel Problem Solving from Nature, PPSN XI. PPSN 2010. Lecture Notes in Computer Science, vol 6238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15844-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-15844-5_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15843-8
Online ISBN: 978-3-642-15844-5
eBook Packages: Computer ScienceComputer Science (R0)