Abstract
A structured 3D SOM is an extension of a Self-Organizing Map from 2D to 3D where a structure has been built into the design of the 3D map. The 3D SOM is a 3x3x3 cube, with a distinct core cube in the center, and 26 exterior cubes around the center. The structured SOM mainly uses the 8 corner cubes among the 26 exterior cubes. Used to build a music archive, the SOM learning algorithm is modified to include a four-step learning and labeling phase. The first phase is meant only to position the music files in their general locations within the core cube. The second phase is meant to position the music files in their respective corner cubes. The third phase is meant to do a fine adjustment of the weight vectors in the core cube. The fourth phase is the labeling of the map and the association of music files to specific nodes in the map. Through the embedded structure of the 3D SOM, a precise measure is developed to measure the quality of the resulting trained SOM (in this case, the music archive), as well as the quality of the different categories/genres of music albums based on a novel measure of the attraction index and the fidelity of music files to their respective music genres.
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References
Kohonen, T.: Self-Organizing Maps. Springer, Berlin (2001)
Lagus, K., Honkela, T., Kaski, S., Kohonen, T.: Websom for Textual Data Mining. Artificial Intelligence Review 13(5-6), 345–364 (1999)
Ultsch, A., Siemon, H.: Kohonen’s self organizing feature maps for exploratory data analysis. In: Proceedings of the International Neural Network Conference, Dordrecht, Netherlands, pp. 305–308 (1990)
Pampalk, E., Rauber, A., Merkl, D.: Content-based organization and visualization of music archives. In: Proceedings of the Tenth ACM International Conference on Multimedia, Juan-les-Pins, France, pp. 570–579 (2002)
Tzanetakis, G., Benning, M., Ness, S., Minifie, D., Livingston, N.: Assistive music browsing using self-organizing maps. In: Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments, Corfu, Greece (2009)
Knees, P., Schedl, M., Pohle, T., Widmer, G.: An innovative three-dimensional user-interface for exploring music collections enriched. In: Proceedings of the 14th Annual ACM International Conference on Multimedia, Santa Barbara, CA, USA, pp. 17–24 (2006)
Manalili, S.: i3DMO: an Interactive 3D Music Organizer, MS Thesis, College of Computer Studies, De La Salle University, Manila, Philippines (2010)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA Data Mining Software: An Update. SIGKDD Explorations 11(1) (2009)
Moerchen, F., Ultsch, A., Thies, M., Loehken, I., Noecker, M., Stamm, C., Efthymiou, N., Kuemmerer, M.: MusicMiner: Visualizing perceptual distances of music as topographical maps, Technical Report Dept. of Mathematics and Computer Science, University of Marburg, Germany (2005)
McKay, C.: Automatic music classification with jMIR. Ph.D. Thesis. McGill University, Canada (2010)
Villmann, T., Der, R., Herrmann, M., Martinetz, T.M.: Topology preservation in self-organizing feature maps – exact definition and measurement. IEEE Trans. Neural Networks 8(2), 256–266 (1997)
Venna, J., Kaski, S.: Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 485–491. Springer, Heidelberg (2001)
Azcarraga, A., Caw, A.: Enhancing SOM digital music archives using Scatter-Gather. In: Proc. Intl. Joint Conference on Neural Networks, Hong Kong, pp. 1833–1839 (2008)
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Azcarraga, A., Manalili, S. (2011). Design of a Structured 3D SOM as a Music Archive. In: Laaksonen, J., Honkela, T. (eds) Advances in Self-Organizing Maps. WSOM 2011. Lecture Notes in Computer Science, vol 6731. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21566-7_19
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DOI: https://doi.org/10.1007/978-3-642-21566-7_19
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