SOMson —SONIFICATION OF MULTIDIMENSIONAL DATA IN KOHONEN MAPS
Author(s)
Linke, Simon
Ziemer, Tim
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Abstract
Kohonen Maps, aka. Self-organizing maps (SOMs) are neural networks
that visualize a high-dimensional feature space on a lowdimensional
map. While SOMs are an excellent tool for data examination
and exploration, they inherently cause a loss of detail.
Visualizations of the underlying data do not integrate well and,
therefore, fail to provide an overall picture. Consequently, we suggest
SOMson, an interactive sonification of the underlying data,
as a data augmentation technique. The sonification increases the
amount of information provided simultaneously by the SOM. Instead
of a user study, we present an interactive online example,
so readers can explore SOMson themselves. Its strengths, weaknesses,
and prospects are discussed.
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Date
2024-06
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Still Image
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Proceedings
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Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0)