TY - CONF AU - Bronner, Fabian AU - Sommer, Christoph ID - 11985 SN - 9781538694282 T2 - 2018 IEEE Vehicular Networking Conference (VNC) TI - Efficient Multi-Channel Simulation of Wireless Communications ER - TY - CHAP AU - Reinold, Peter AU - Meyer, Norbert AU - Buse, Dominik AU - Klingler, Florian AU - Sommer, Christoph AU - Dressler, Falko AU - Eisenbarth, Markus AU - Andert, Jakob ID - 12043 SN - 2198-7432 T2 - Proceedings TI - Verkehrssimulation im Hardware-in-the-Loop-Steuergerätetest ER - TY - CHAP AU - Sommer, Christoph AU - Eckhoff, David AU - Brummer, Alexander AU - Buse, Dominik S. AU - Hagenauer, Florian AU - Joerer, Stefan AU - Segata, Michele ID - 12072 SN - 2522-8595 T2 - Recent Advances in Network Simulation TI - Veins: The Open Source Vehicular Network Simulation Framework ER - TY - CONF AU - Yigitbas, Enes AU - Heindörfer, Joshua AU - Engels, Gregor ID - 12076 T2 - Proceedings of the Mensch und Computer 2019 (MuC ’19) TI - A Context-aware Virtual Reality First Aid Training Application ER - TY - GEN AB - Die Komplexität von Steuersystemen gewinnt in der Debatte um den internationalen Steuerwettbewerb zunehmend an Bedeutung. Im vorliegenden Beitrag erfolgt, basierend auf den Daten, die dem Tax Complexity Index (www.taxcomplexity.org) zugrunde liegen, eine umfassende Gegenüberstellung der Komplexität der Steuersysteme von Deutschland und Öster-reich unter Berücksichtigung der Mittelwerte aller Länder. Die Steuergesetze weisen sowohl in Deutschland als auch in Österreich einen verhältnismäßig hohen Grad an Komplexität auf. Bei den steuerlichen Rahmenbedingungen fällt der Grad an Komplexität in beiden Ländern dagegen niedrig aus, wobei Österreich im Durchschnitt weniger komplex ist als Deutschland. AU - Hoppe, Thomas AU - Rechbauer, Martina AU - Sturm, Susann ID - 12077 TI - Steuerkomplexität im Vergleich zwischen Deutschland und Österreich – Eine Analyse des Status quo ER - TY - CONF AU - Feldkord, Björn AU - Knollmann, Till AU - Malatyali, Manuel AU - Meyer auf der Heide, Friedhelm ID - 12870 T2 - Proceedings of the 17th Workshop on Approximation and Online Algorithms (WAOA) TI - Managing Multiple Mobile Resources ER - TY - CONF AB - We propose a training scheme to train neural network-based source separation algorithms from scratch when parallel clean data is unavailable. In particular, we demonstrate that an unsupervised spatial clustering algorithm is sufficient to guide the training of a deep clustering system. We argue that previous work on deep clustering requires strong supervision and elaborate on why this is a limitation. We demonstrate that (a) the single-channel deep clustering system trained according to the proposed scheme alone is able to achieve a similar performance as the multi-channel teacher in terms of word error rates and (b) initializing the spatial clustering approach with the deep clustering result yields a relative word error rate reduction of 26% over the unsupervised teacher. AU - Drude, Lukas AU - Hasenklever, Daniel AU - Haeb-Umbach, Reinhold ID - 12874 T2 - ICASSP 2019, Brighton, UK TI - Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source Separation ER - TY - CONF AB - Signal dereverberation using the Weighted Prediction Error (WPE) method has been proven to be an effective means to raise the accuracy of far-field speech recognition. First proposed as an iterative algorithm, follow-up works have reformulated it as a recursive least squares algorithm and therefore enabled its use in online applications. For this algorithm, the estimation of the power spectral density (PSD) of the anechoic signal plays an important role and strongly influences its performance. Recently, we showed that using a neural network PSD estimator leads to improved performance for online automatic speech recognition. This, however, comes at a price. To train the network, we require parallel data, i.e., utterances simultaneously available in clean and reverberated form. Here we propose to overcome this limitation by training the network jointly with the acoustic model of the speech recognizer. To be specific, the gradients computed from the cross-entropy loss between the target senone sequence and the acoustic model network output is backpropagated through the complex-valued dereverberation filter estimation to the neural network for PSD estimation. Evaluation on two databases demonstrates improved performance for on-line processing scenarios while imposing fewer requirements on the available training data and thus widening the range of applications. AU - Heymann, Jahn AU - Drude, Lukas AU - Haeb-Umbach, Reinhold AU - Kinoshita, Keisuke AU - Nakatani, Tomohiro ID - 12875 T2 - ICASSP 2019, Brighton, UK TI - Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online ASR ER - TY - CONF AB - In this paper, we present libDirectional, a MATLAB library for directional statistics and directional estimation. It supports a variety of commonly used distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped Cauchy distributions. Furthermore, various distributions on higher-dimensional manifolds such as the unit hypersphere and the hypertorus are available. Based on these distributions, several recursive filtering algorithms in libDirectional allow estimation on these manifolds. The functionality is implemented in a clear, well-documented, and object-oriented structure that is both easy to use and easy to extend. AU - Kurz, Gerhard AU - Gilitschenski, Igor AU - Pfaff, Florian AU - Drude, Lukas AU - Hanebeck, Uwe D. AU - Haeb-Umbach, Reinhold AU - Siegwart, Roland Y. ID - 12876 T2 - Journal of Statistical Software 89(4) TI - Directional Statistics and Filtering Using libDirectional ER - TY - CONF AB - By distributing the computational load over the nodes of a Wireless Acoustic Sensor Network (WASN), the real-time capability of the TRINICON (TRIple-N-Independent component analysis for CONvolutive mixtures) framework for Blind Source Separation (BSS) can be ensured, even if the individual network nodes are not powerful enough to run TRINICON in real-time by themselves. To optimally utilize the limited computing power and data rate in WASNs, the MARVELO (Multicast-Aware Routing for Virtual network Embedding with Loops in Overlays) framework is expanded for use with TRINICON, while a feature-based selection scheme is proposed to exploit the most beneficial parts of the input signal for adapting the demixing system. The simulation results of realistic scenarios show only a minor degradation of the separation performance even in heavily resource-limited situations. AU - Guenther, Michael AU - Afifi, Haitham AU - Brendel, Andreas AU - Karl, Holger AU - Kellermann, Walter ID - 12880 T2 - 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) (WASPAA 2019) TI - Sparse Adaptation of Distributed Blind Source Separation in Acoustic Sensor Networks ER -