TY - CHAP A1 - Aspöck, Lukas A1 - Brinkmann, Fabian A1 - Ackermann, David A1 - Weinzierl, Stefan A1 - Vorländer, Michael ED - Seeber, Bernhard T1 - Eine Datenbank zur Validierung von akustischen Simulationen T2 - Fortschritte der Akustik – DAGA 2018: 44. Jahrestagung für Akustik, 19.-22. März 2018 in München Y1 - 2018 SN - 978-3-939296-13-3 SP - 1337 EP - 1337 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - CHAP A1 - Böhm, Christoph A1 - Ackermann, David A1 - Weinzierl, Stefan ED - Seeber, Bernhard T1 - Eine mehrkanalige und nachhallfreie Aufnahme von Beethovens 8. Sinfonie T2 - Fortschritte der Akustik – DAGA 2018 - 44. Jahrestagung für Akustik 19.-22. März 2018, München Y1 - 2018 UR - https://doi.org/10.14279/depositonce-8678 SN - 978-3-939296-14-0 U6 - https://doi.org/10.14279/depositonce-8678 SP - 112 EP - 115 PB - Deutsche Gesellschaft für Akustik e.V. CY - Berlin ER - TY - JOUR A1 - Kob, Malte A1 - Ackermann, David A1 - Weinzierl, Stefan A1 - Zotter, Franz T1 - Dynamische Richtwirkung von Musikinstrumenten JF - Akustik Journal Y1 - 2018 UR - https://www.dega-akustik.de/fileadmin/dega-akustik.de/publikationen/akustik-journal/18-03/akustik_journal_2018_03_online_artikel3.pdf SN - 2569-1597 VL - 3 SP - 28 EP - 38 PB - Deutsche Gesellschaft für Akustik e.V. ER - TY - GEN A1 - Böhm, Christoph A1 - Ackermann, David A1 - Weinzierl, Stefan T1 - A Multi-channel Anechoic Orchestra Recording of Beethoven's Symphony No. 8 op. 93: Audio Recordings and Accompanying Data N2 - This data set contains an excerpt of the anechoic recording of the Symphony No. 8 in F Major, Op. 93 by Ludwig van Beethoven, performed by the "Orchester Wiener Akademie" conducted by Martin Haselböck. It was recorded in the anechoic chamber of the TU Berlin. KW - anechoic audio KW - auralization KW - virtual acoustics Y1 - 2018 UR - https://doi.org/10.17743/jaes.2020.0056 U6 - https://doi.org/10.14279/depositonce-6729.2 N1 - DFG, FOR 1557, Simulation and Evaluation of Acoustical Environments (SEACEN) PB - TU Berlin CY - Berlin ER - TY - GEN A1 - Ackermann, David A1 - Ilse, Michael A1 - Grigoriev, Dmitry A1 - Lepa, Steffen A1 - Pelzer, Sönke A1 - Vorländer, Michael A1 - Weinzierl, Stefan T1 - A Ground Truth on Room Acoustical Analysis and Perception (GRAP) N2 - A database of 35 virtual room models was created that can serve as a ground truth for the future development of room acoustical parameters beyond ISO 3382-1. Some of the room models are based on existing performance venues, however without yielding a perfect match of measured and simulated acoustical parameters. Others are artificial designs which were made to systematically cover a wide variety of room acoustical properties. Each of the 35 acoustical environments included in the GRAP database consists of three components: (1) the room model which specifies source and receiver positions and the acoustic properties of the surfaces, (2) the simulated monaural and binaural impulse responses, as well as (3) the item and factor scores, based on a listening test using the Room Acoustic Quality Inventory (RAQI). KW - room acoustical analysis KW - virtual acoustic KW - room acoustic simulation Y1 - 2018 U6 - https://doi.org/10.14279/depositonce-7003.4 N1 - DFG, FOR 1557, Simulation and Evaluation of Acoustical Environments (SEACEN) PB - TU Berlin CY - Berlin ER - TY - GEN A1 - Ackermann, David A1 - Böhm, Christoph A1 - Weinzierl, Stefan T1 - A Database on Musicians’ Movements During Musical Performances N2 - The movements of 20 musicians playing 11 different musical instruments, including all standard orchestral instruments, were captured during solo performances by means of a motion capturing system under concert-like conditions. KW - Musical Acoustics KW - Virtual Acoustc KW - Room Acoustic Y1 - 2018 U6 - https://doi.org/10.14279/depositonce-7469 N1 - DFG, FOR 1557, Simulation and Evaluation of Acoustical Environments (SEACEN) PB - TU Berlin ER - TY - CHAP A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Krömker, Detlef ED - Schroeder, Ulrik T1 - Track every move of your students: log files for Learning Analytics from mobile screen recordings T2 - DeLFI 2018: die 16. E-Learning Fachtagung Informatik der Gesellschaft für Informatik e.V., 10.-12. September 2018, Frankfurt am Main, Deutschland N2 - One of the main data sources for Learning Analytics are Learning Management Systems (LMS).These log files are limited though to interactions within the LMS and cannot take into account interactions of students in other applications and software in a digital learning environment. In this paper, we present an approach for generating log files based on mobile screen recordings as a data source for Learning Analytics. Logging mobile application usage is limited to rather general system events unless you have access to the source code of the operating system or applications.To address this we generate log files from mobile screen recordings by applying computer vision and machine learning methods to detect individually defined events. In closing, we discuss how these log files can be used as a data source for Learning Analytics and relevant ethical concerns. KW - Learning Analytics KW - mobile screen recordings KW - Computer Vison KW - data sources KW - Human Computer Interaction KW - log files Y1 - 2018 SN - 9783885796787 U6 - https://doi.org/10.18154/RWTH-2018-229913 VL - Lecture Notes in Informatics(LNI)-Proceedings, Vol. P-284 SP - 231 EP - 242 PB - Köllen Druck+Verlag CY - Bonn ER - TY - CHAP A1 - Krieter, Philipp A1 - Breiter, Andreas ED - Balie, Lynne ED - Oliver, Nuria T1 - Analyzing mobile application usage: generating log files from mobile screen recordings T2 - Proceedings of the 20th International Conference on Human-Computer Interaction with Mobile Devices and Services, Barcelona Spain, September 3 - 6, 2018 Y1 - 2018 SN - 978-1-4503-5898-9 U6 - https://doi.org/10.1145/3229434.3229450 SP - 1 EP - 10 PB - ACM CY - New York ER - TY - PAT A1 - Lessmann, Stephanie A1 - Meuter, Mirko A1 - Mueller, Dennis T1 - Method to determine distance of an object from an automated vehicle with a monocular device N2 - A method of determining the distance of an object from an automated vehicle based on images taken by a monocular image acquiring device. The object is recognized with an object-class by means of an image processing system. Respective position data are determined from the images using a pinhole camera model based on the object-class. Position data indicating in world coordinates the position of a reference point of the object with respect to the plane of the road is used with a scaling factor of the pinhole camera model estimated by means of a Bayes estimator using the position data as observations and under the assumption that the reference point of the object is located on the plane of the road with a predefined probability. The distance of the object from the automated vehicle is calculated from the estimated scaling factor using the pinhole camera model. Y1 - 2018 UR - https://depatisnet.dpma.de/DepatisNet/depatisnet?action=pdf&docid=US000009862318B2&xxxfull=1 ER - TY - CHAP A1 - Huldtgren, Alina ED - Kuckert-Wöstheinrich, Andrea ED - Forsch, Georg ED - Wagner, Holger T1 - Medien, Design und Demenz T2 - Ethik Design Demenz eine multidisziplinäre Annäherung ; a multidisciplinary approach KW - Demenz KW - Design KW - Ethik Y1 - 2018 SN - 978-3-00-057748-2 SP - 44 EP - 57 PB - St. Augustinus Fachkliniken CY - Neuss ER -