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dc.contributor.authorMeinecke, Christoferen_US
dc.contributor.authorSchebera, Jeremiasen_US
dc.contributor.authorEschrich, Jakoben_US
dc.contributor.authorWiegreffe, Danielen_US
dc.contributor.editorKrone, Michaelen_US
dc.contributor.editorLenti, Simoneen_US
dc.contributor.editorSchmidt, Johannaen_US
dc.date.accessioned2022-06-02T15:29:17Z
dc.date.available2022-06-02T15:29:17Z
dc.date.issued2022
dc.identifier.isbn978-3-03868-185-4
dc.identifier.urihttps://doi.org/10.2312/evp.20221129
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evp20221129
dc.description.abstractRap music is one of the biggest music genres in the world today. Since the early days of rap music, references not only to pop culture but also to other rap artists have been an integral part of the lyrics' artistry. In addition, rap musicians reference each other by adopting fragments of lyrics, for example, to give credit. This kind of text reuse can be used to create connections between individual artists. Due to the large amount of lyrics, only automated detection methods can efficiently detect similarities between the songs and the artists. Here, we present a visualization system for analyzing rap music lyrics. We also trained a network tailored specifically for rap lyrics to compute similarities in lyrics. Here a video of the prototype can be seen.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing --> Visualization application domains; Visualization systems and tools; Applied computing --> Sound and music computing; Document management and text processing
dc.subjectHuman centered computing
dc.subjectVisualization application domains
dc.subjectVisualization systems and tools
dc.subjectApplied computing
dc.subjectSound and music computing
dc.subjectDocument management and text processing
dc.titleVisualizing Similarities between American Rap-Artistsen_US
dc.description.seriesinformationEuroVis 2022 - Posters
dc.description.sectionheadersPosters
dc.identifier.doi10.2312/evp.20221129
dc.identifier.pages95-97
dc.identifier.pages3 pages


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License