Auflistung nach Autor:in "Schrapel, Maximilian"
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- ZeitschriftenartikelEnvironZen: Immersive Soundscapes via Augmented Footstep Sounds in Urban Areas(i-com: Vol. 21, No. 2, 2022) Schrapel, Maximilian; Happe, Janko; Rohs, MichaelUrban environments are often characterized by loud and annoying sounds. Noise-cancelling headphones can suppress negative influences and superimpose the acoustic environment with audio-augmented realities (AAR). So far, AAR exhibited limited interactivity, e. g., being influenced by the location of the listener. In this paper we explore the superimposition of synchronized, augmented footstep sounds in urban AAR environments with noise-cancelling headphones. In an online survey, participants rated different soundscapes and sound augmentations. This served as a basis for selecting and designing soundscapes and augmentations for a subsequent in-situ field study in an urban environment with 16 participants. We found that the synchronous footstep feedback of our application EnvironZen contributes to creating a relaxing and immersive soundscape. Furthermore, we found that slightly delaying footstep feedback can be used to slow down walking and that particular footstep sounds can serve as intuitive navigation cues.
- KonferenzbeitragSign H3re: Symbol and X-Mark Writer Identification Using Audio and Motion Data from a Digital Pen(Mensch und Computer 2022 - Tagungsband, 2022) Schrapel, Maximilian; Grannemann, Dennis; Rohs, MichaelAlthough in many cases contracts can be made or ended digitally, laws require handwritten signatures in certain cases. Forgeries are a major challenge with digital contracts, as their validity is not always immediately apparent without forensic methods. Illiteracy or disabilities may result in a person being unable to write their full name. In this case x-mark signatures are used, which require a witness for validity. In cases of suspected fraud, the relationship of the witnesses must be questioned, which involves a great amount of effort. In this paper we use audio and motion data from a digital pen to identify users via handwritten symbols. We evaluated the performance our approach for 19 symbols in a study with 30 participants. We found that x-marks offer fewer individual features than other symbols like arrows or circles. By training on three samples and averaging three predictions we reach a mean F1-score of F 1 = 0.87, using statistical and spectral features fed into SVMs