Abstract
Classification algorithms are gaining more and more importance in many fields such as Artificial Intelligence, Information Retrieval, Data Mining and Machine Vision. Many classification algorithms have emerged, belonging to different families, among which the tree-based and the clustering-based ones. Such extensive availability of classifiers makes the selection of the optimal one per case a rather complex task. In this paper, we aim to address this issue by conducting extensive experiments in a music information retrieval application, specifically with respect to music genre queries, in order to compare the performance of two state-of-the-art classifiers belonging to the formerly mentioned two classes of classification algorithms, namely, TreeQ and LVQ, respectively, using a variety of music features for such a task. The deployed performance metrics are extensive: accuracy, precision, recall, Fmeasure, confidence. Conclusions on the best performance of either classifier to support music genre queries are finally drawn.
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References
Jonathan T. Foote, TreeQ Manual V0.8, September, 2003
T. Kohonen, H. Hynninen, J. Kangas, H. Laaksonen, and K. Torkkola. LVQ-PAK: The learning vector quantization program package, Technical Report A30, Helsinki University of Technology, Laboratory of Computer and Information Science, FIN-02150 Espoo, Finland, 1996
Jonathan T. Foote, Content-based retrieval of music and audio, Multimedia Storage and Archiving Systems II, Proceedings of SPIE, 1997
Jonathan T. Foote, An overview of audio information retrieval, Multimedia Syst., Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1999
Music retrieval demo using open-source software package TreeQ by Jonathan T. Foote, http://www.rotorbrain.com/foote/musicr/doc16.html
Forecasting with artificial neural networks, http://www.neural-forecasting.com
Helsinki University of Technology — Neural Networks Research Centre, http://www.cis.hut.fi/research/som_lvq_pak.shtml
The HTK Toolkit, http://htk.eng.cam.ac.uk/
Steve Young et al, The HTK Book (1995–1999 Microsoft Corporation, 2001–2006 Cambridge University Engineering Department)
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© 2007 International Federation for Information Processing
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Charami, M., Halloush, R., Tsekeridou, S. (2007). Performance Evaluation of TreeQ and LVQ Classifiers for Music Information Retrieval. In: Boukis, C., Pnevmatikakis, A., Polymenakos, L. (eds) Artificial Intelligence and Innovations 2007: from Theory to Applications. AIAI 2007. IFIP The International Federation for Information Processing, vol 247. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74161-1_36
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DOI: https://doi.org/10.1007/978-0-387-74161-1_36
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