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pro vyhledávání: '"Heyen, Frank"'
Autor:
Klötzl, Daniel, Krake, Tim, Heyen, Frank, Becher, Michael, Koch, Maurice, Weiskopf, Daniel, Kurzhals, Kuno
The depiction of scanpaths from mobile eye-tracking recordings by thumbnails from the stimulus allows the application of visual computing to detect areas of interest in an unsupervised way. We suggest using nonnegative matrix factorization (NMF) to i
Externí odkaz:
http://arxiv.org/abs/2404.03417
We propose different methods for alternative representation and visual augmentation of sheet music that help users gain an overview of general structure, repeating patterns, and the similarity of segments. To this end, we explored mapping the overall
Externí odkaz:
http://arxiv.org/abs/2308.06140
We propose a data-driven approach to music instrument practice that allows studying patterns and long-term trends through visualization. Inspired by life logging and fitness tracking, we imagine musicians to record their practice sessions over the sp
Externí odkaz:
http://arxiv.org/abs/2203.13320
We propose a 3D immersive visualization environment for analyzing the right hand movements of a cello player. To achieve this, we track the position and orientation of the cello bow and record audio. As movements mostly occur in a shallow volume and
Externí odkaz:
http://arxiv.org/abs/2203.13316
Autor:
Rijken, Gerrit J., Cutura, Rene, Heyen, Frank, Sedlmair, Michael, Correll, Michael, Dykes, Jason, Smit, Noeska
The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the sp
Externí odkaz:
http://arxiv.org/abs/2109.01688
Akademický článek
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We propose a visual approach for interactive, AI-assisted composition that serves as a compromise between fully automatic and fully manual composition. Instead of generating a whole piece, the AI takes on the role of an assistant that generates short
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5208f59bdeeaf719ef63be2ad170651f
Akademický článek
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Publikováno v:
Journal of Visualization; Feb2023, Vol. 26 Issue 1, p161-176, 16p
Autor:
Heyen, Frank
We present a batch training and visualization system that enables users to visually compare different classifiers and parameter configurations in their performance and behavior. Our approach is plugin-based and classifier-agnostic and allows users to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b3eda8207e3009b1850d212a85ee3e4
http://elib.uni-stuttgart.de/handle/11682/10745
http://elib.uni-stuttgart.de/handle/11682/10745