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dc.contributor.authorRitter, Christianen_US
dc.contributor.authorAltenhofen, Christianen_US
dc.contributor.authorZeppelzauer, Matthiasen_US
dc.contributor.authorKuijper, Arjanen_US
dc.contributor.authorSchreck, Tobiasen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.editorChristian Tominski and Tatiana von Landesbergeren_US
dc.date.accessioned2018-06-02T17:57:02Z
dc.date.available2018-06-02T17:57:02Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-064-2
dc.identifier.urihttp://dx.doi.org/10.2312/eurova.20181109
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20181109
dc.description.abstractWe present an interactive visual music classification tool that will allow users to automatically structure music collections in a personalized way. With our approach, users play an active role in an iterative process of building classification models, using different interactive interfaces for labeling songs. The interactive tool conflates interfaces for the detailed analysis at different granularities, i.e., audio features, music songs, as well as classification results at a glance. Interactive labeling is provided with three complementary interfaces, combining model-centered and human-centered labeling-support principles. A clean visual design of the individual interfaces depicts complex model characteristics for experts, and indicates our work-inprogress towards the abilities of non-experts. The result of a preliminary usage scenario shows that, with our system, hardly any knowledge about machine learning is needed to create classification models of high accuracy with less than 50 labels.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisualization application domains
dc.subjectComputing methodologies
dc.subjectMachine learning
dc.titlePersonalized Visual-Interactive Music Classificationen_US
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.description.sectionheadersApplications
dc.identifier.doi10.2312/eurova.20181109
dc.identifier.pages31-35


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