Rhythmicalizer
| dc.contributor.author | Meyer-Sickendiek, Burkhard | |
| dc.contributor.author | Hussein, Hussein | |
| dc.contributor.author | Baumann, Timo | |
| dc.contributor.editor | Eibl, Maximilian | |
| dc.contributor.editor | Gaedke, Martin | |
| dc.date.accessioned | 2017-08-28T23:47:41Z | |
| dc.date.available | 2017-08-28T23:47:41Z | |
| dc.date.issued | 2017 | |
| dc.description.abstract | The most important development in modern and postmodern poetry is the replacement of traditional meter by new rhythmical patterns. Ever since Walt Whitman's Leaves of Grass (1855), modern (nineteenth-to twenty-first-century) poets have been searching for novel forms of prosody, accent, rhythm, and intonation. Along with the rejection of older metrical units such as the iamb or trochee, a structure of lyrical language was developed that renounced traditional forms like rhyme and meter. This development is subsumed under the term free verse prosody. Our project will test this theory by applying machine learning or deep learning techniques to a corpus of modern and postmodern poems as read aloud by the original authors. To this end, we examine “lyrikline”, the most famous online portal for spoken poetry. First, about 17 different patterns being characteristic for the lyrikline-poems have been identified by the philological scholar of this project. This identification was based on a certain philological method including three different steps: a) grammetrical ranking; b) rhythmic phrasing; and c) mapping rubato and prosodic phrasing. In this paper we will show how to combine this philological and a digital analysis by using the prosody detection available in speech processing technology. In order to analyse the data, we want to use different tools for the following tasks: PoS-tagging, alignment, intonation, phrases and pauses, and tempo. We also analyzed the lyrikline-data by identifying the occurrence of the mentioned patterns. This analysis is a first step towards an automatic classification based on machine learning or deep learning techniques. | en |
| dc.identifier.doi | 10.18420/in2017_218 | |
| dc.identifier.isbn | 978-3-88579-669-5 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2017 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-275 | |
| dc.subject | modern and postmodern poetry | |
| dc.subject | free verse prosody | |
| dc.subject | rhythmic patterns | |
| dc.subject | automatic classification | |
| dc.title | Rhythmicalizer | en |
| dc.title.subtitle | Data Analysis for the Identification of Rhythmic Patterns in Readout Poetry (Work-in-Progress) | en |
| gi.citation.endPage | 2200 | |
| gi.citation.startPage | 2189 | |
| gi.conference.date | 25.-29. September 2017 | |
| gi.conference.location | Chemnitz | |
| gi.conference.sessiontitle | Deep Learning in heterogenen Datenbeständen |
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