@misc{10706, author = {{Makeswaran, Vignesh}}, publisher = {{Paderborn University}}, title = {{{Operating System Support for Reconfigurable Cache}}}, year = {{2016}}, } @misc{10707, author = {{Ibne Ashraf, Ishraq}}, publisher = {{Paderborn University}}, title = {{{Private/Shared Data Classification and Implementation for a Multi-Softcore Platform}}}, year = {{2016}}, } @inproceedings{10712, author = {{Meisner, Sebastian and Platzner, Marco}}, booktitle = {{Reconfigurable Computing and FPGAs (ReConFig), 2016 International Conference on}}, pages = {{1--8}}, title = {{{Thread Shadowing: On the Effectiveness of Error Detection at the Hardware Thread Level}}}, doi = {{10.1109/ReConFig.2016.7857193}}, year = {{2016}}, } @misc{10755, author = {{Schmidt, Marco}}, publisher = {{Paderborn University}}, title = {{{Konzeption und Implementierung einer digitalen Ansteuerung für den Betrieb einer elektrischen Sendereinheit für induktive Energieübertragung}}}, year = {{2016}}, } @book{10758, author = {{Squillero, Giovanni and Burelli, Paolo and M. Mora, Antonio and Agapitos, Alexandros and S. Bush, William and Cagnoni, Stefano and Cotta, Carlos and De Falco, Ivanoe and Della Cioppa, Antonio and Divina, Federico and Eiben, A.E. and I. Esparcia-Alc{\'a}zar, Anna and Fern{\'a}ndez de Vega, Francisco and Glette, Kyrre and Haasdijk, Evert and Ignacio Hidalgo, J. and Kampouridis, Michael and Kaufmann, Paul and Mavrovouniotis, Michalis and Thanh Nguyen, Trung and Schaefer, Robert and Sim, Kevin and Tarantino, Ernesto and Urquhart, Neil and Zhang (editors), Mengjie}}, publisher = {{Springer}}, title = {{{Applications of Evolutionary Computation - 19th European Conference, EvoApplications}}}, volume = {{9597}}, year = {{2016}}, } @inproceedings{10766, author = {{Ghribi, Ines and Ben Abdallah, Riadh and Khalgui, Mohamed and Platzner, Marco}}, booktitle = {{Proceedings of the 30th European Simulation and Modelling Conference (ESM)}}, title = {{{RCo-Design: New Visual Environment for Reconfigurable Embedded Systems}}}, year = {{2016}}, } @inproceedings{10768, author = {{Ghribi, Ines and Ben Abdallah, Riadh and Khalgui, Mohamed and Platzner, Marco}}, booktitle = {{Proceedings of the 11th International Conference on Software Engineering and Applications (ICSOFT-EA)}}, pages = {{185--195}}, title = {{{New Co-design Methodology for Real-time Embedded Systems}}}, year = {{2016}}, } @article{10769, author = {{Ghasemzadeh Mohammadi, Hassan and Gaillardon, Pierre-Emmanuel and De Micheli, Giovanni}}, journal = {{IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems}}, number = {{99}}, pages = {{1--1}}, publisher = {{IEEE}}, title = {{{Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation}}}, doi = {{10.1109/TCAD.2016.2547908}}, volume = {{PP}}, year = {{2016}}, } @misc{10781, author = {{Hermansen, Sven}}, publisher = {{Paderborn University}}, title = {{{Custom Memory Controller for ReconOS}}}, year = {{2016}}, } @misc{10785, author = {{Fürnkranz, J. and Hüllermeier, Eyke}}, booktitle = {{Encyclopedia of Machine Learning and Data Mining}}, editor = {{Sammut, C. and Webb, G.I.}}, publisher = {{Springer}}, title = {{{Preference Learning}}}, year = {{2016}}, } @misc{1082, author = {{Handirk, Tobias}}, publisher = {{Universität Paderborn}}, title = {{{Über die Rolle von Informationen in Verkehrsnetzwerken}}}, year = {{2016}}, } @inproceedings{11738, abstract = {{In this contribution we investigate a priori signal-to-noise ratio (SNR) estimation, a crucial component of a single-channel speech enhancement system based on spectral subtraction. The majority of the state-of-the art a priori SNR estimators work in the power spectral domain, which is, however, not confirmed to be the optimal domain for the estimation. Motivated by the generalized spectral subtraction rule, we show how the estimation of the a priori SNR can be formulated in the so called generalized SNR domain. This formulation allows to generalize the widely used decision directed (DD) approach. An experimental investigation with different noise types reveals the superiority of the generalized DD approach over the conventional DD approach in terms of both the mean opinion score - listening quality objective measure and the output global SNR in the medium to high input SNR regime, while we show that the power spectrum is the optimal domain for low SNR. We further develop a parameterization which adjusts the domain of estimation automatically according to the estimated input global SNR. Index Terms: single-channel speech enhancement, a priori SNR estimation, generalized spectral subtraction}}, author = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2016, San Francisco, USA}}, title = {{{A Priori SNR Estimation Using a Generalized Decision Directed Approach}}}, year = {{2016}}, } @inproceedings{11743, abstract = {{This contribution introduces a novel causal a priori signal-to-noise ratio (SNR) estimator for single-channel speech enhancement. To exploit the advantages of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM) domain is introduced where an a priori SNR estimation is carried out. In this domain, the NAOM coefficients of noise and clean speech signals are modeled by a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the parameters of the noise model are calculated from the noise power spectral density estimates, the speechWMM parameters are estimated from the noisy signal by applying a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate of the a priori SNR is developed. The experiments in different noisy environments show the superiority of the proposed estimator compared to the well-known decision-directed approach in terms of estimation error, estimator variance and speech quality of the enhanced signals when used for speech enhancement.}}, author = {{Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold}}, booktitle = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}}, title = {{{A Priori SNR Estimation Using Weibull Mixture Model}}}, year = {{2016}}, } @inproceedings{11744, abstract = {{A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component.}}, author = {{Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}}, title = {{{Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs}}}, year = {{2016}}, } @inproceedings{11751, author = {{Drude, Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Blind Speech Separation based on Complex Spherical k-Mode Clustering}}}, year = {{2016}}, } @inproceedings{11756, abstract = {{Although complex-valued neural networks (CVNNs) â?? networks which can operate with complex arithmetic â?? have been around for a while, they have not been given reconsideration since the breakthrough of deep network architectures. This paper presents a critical assessment whether the novel tool set of deep neural networks (DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs making inroads in speech enhancement tasks, the use of complex-valued input data, specifically the short-time Fourier transform coefficients, is an obvious consideration. In particular when it comes to performing tasks that heavily rely on phase information, such as acoustic beamforming, complex-valued algorithms are omnipresent. In this contribution we recapitulate backpropagation in CVNNs, develop complex-valued network elements, such as the split-rectified non-linearity, and compare real- and complex-valued networks on a beamforming task. We find that CVNNs hardly provide a performance gain and conclude that the effort of developing the complex-valued counterparts of the building blocks of modern deep or recurrent neural networks can hardly be justified.}}, author = {{Drude, Lukas and Raj, Bhiksha and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2016, San Francisco, USA}}, title = {{{On the appropriateness of complex-valued neural networks for speech enhancement}}}, year = {{2016}}, } @inproceedings{11771, abstract = {{This paper is concerned with speech presence probability estimation employing an explicit model of the temporal and spectral correlations of speech. An undirected graphical model is introduced, based on a Factor Graph formulation. It is shown that this undirected model cures some of the theoretical issues of an earlier directed graphical model. Furthermore, we formulate a message passing inference scheme based on an approximate graph factorization, identify this inference scheme as a particular message passing schedule based on the turbo principle and suggest further alternative schedules. The experiments show an improved performance over speech presence probability estimation based on an IID assumption, and a slightly better performance of the turbo schedule over the alternatives.}}, author = {{Glarner, Thomas and Mahdi Momenzadeh, Mohammad and Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}}, title = {{{Factor Graph Decoding for Speech Presence Probability Estimation}}}, year = {{2016}}, } @inproceedings{11812, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Neural Network Based Spectral Mask Estimation for Acoustic Beamforming}}}, year = {{2016}}, } @inproceedings{11829, abstract = {{This contribution investigates Direction of Arrival (DoA) estimation using linearly arranged microphone arrays. We are going to develop a model for the DoA estimation error in a reverberant scenario and show the existence of a bias, that is a consequence of the linear arrangement and limited field of view (FoV) bias: First, the limited FoV leading to a clipping of the measurements, and, second, the angular distribution of the signal energy of the reflections being non-uniform. Since both issues are a consequence of the linear arrangement of the sensors, the bias arises largely independent of the kind of DoA estimator. The experimental evaluation demonstrates the existence of the bias for a selected number of DoA estimation methods and proves that the prediction from the developed theoretical model matches the simulation results.}}, author = {{Jacob, Florian and Haeb-Umbach, Reinhold}}, booktitle = {{12. ITG Fachtagung Sprachkommunikation (ITG 2016)}}, title = {{{On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays}}}, year = {{2016}}, } @inproceedings{11834, abstract = {{We present a system for the 4th CHiME challenge which significantly increases the performance for all three tracks with respect to the provided baseline system. The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural network to estimate signal statistics. These then steer a Generalized Eigenvalue beamformer. The back-end consists of a 22 layer deep Wide Residual Network and two extra BLSTM layers. Working on a whole utterance instead of frames allows us to refine Batch-Normalization. We also train our own BLSTM-based language model. Adding a discriminative speaker adaptation leads to further gains. The final system achieves a word error rate on the six channel real test data of 3.48%. For the two channel track we achieve 5.96% and for the one channel track 9.34%. This is the best reported performance on the challenge achieved by a single system, i.e., a configuration, which does not combine multiple systems. At the same time, our system is independent of the microphone configuration. We can thus use the same components for all three tracks.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{Computer Speech and Language}}, title = {{{Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition}}}, year = {{2016}}, }