@inproceedings{6638, author = {{Krauter, Stefan}}, booktitle = {{VDE-Proceedings of NEIS 2017 – Conference on Sustainable Energy Supply and Energy Storage Systems by IEEE-PES. Hamburg (Deutschland), 21.–22. September, 2017.}}, location = {{Hamburg}}, title = {{{Comparison of Conversion Efficiencies and Energy Yields of Micro-Inverters for Photovoltaic Modules}}}, year = {{2017}}, } @inproceedings{6639, author = {{Krauter, Stefan and Ameli, Ali}}, booktitle = {{VDE-Proceedings of NEIS 2017 – Conference on Sustainable Energy Supply and Energy Storage Systems by IEEE-PES. Hamburg (Deutschland), 21.–22. September, 2017.}}, location = {{Hamburg}}, title = {{{Smart Charging Management System of Plugged-in EVs for Optimal Operation of Future Power Systems.}}}, year = {{2017}}, } @inproceedings{6640, author = {{Krauter, Stefan}}, booktitle = {{Proceedings of the 11th International Conference for Renewable Energy Storage, 14-16 March 2017, Düsseldorf, Germany.}}, location = {{Düsseldorf, Germany.}}, title = {{{Minimizing storage costs: Simple and effective methods to match PV with grid load, including shift of holiday period}}}, year = {{2017}}, } @inproceedings{6641, author = {{Khatibi, Arash and Bendfeld, Jörg and Bermpohl, Wolfgang and Krauter, Stefan}}, booktitle = {{Proceedings of the 33rd European Photovoltaic Solar Energy Conference, Amsterdam, (Niederlande), 25.-29. Sept. 2017}}, location = {{Amsterdam}}, title = {{{Introduction of an Advanced Method for Testing of Battery Charge Controllers for Off-Grid PV Systems}}}, year = {{2017}}, } @inproceedings{6642, author = {{Khatibi, Arash and Bendfeld, Jörg and Bermpohl, Wolfgang and Krauter, Stefan}}, booktitle = {{Proceedings of the 33rd European Photovoltaic Solar Energy Conference, Amsterdam, (Niederlande), 25.-29. Sept. 2017}}, location = {{Amsterdam}}, title = {{{Testing and Analysis of Battery Charge Controllers for Off-Grid PV Systems}}}, year = {{2017}}, } @article{680, author = {{Peter, Manuel and Hildebrandt, Andre and Schlickriede, Christian and Gharib, Kimia and Zentgraf, Thomas and Förstner, Jens and Linden, Stefan}}, issn = {{1530-6984}}, journal = {{Nano Letters}}, keywords = {{tet_topic_opticalantenna}}, number = {{7}}, pages = {{4178--4183}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Directional Emission from Dielectric Leaky-Wave Nanoantennas}}}, doi = {{10.1021/acs.nanolett.7b00966}}, volume = {{17}}, year = {{2017}}, } @inproceedings{11717, abstract = {{In this work, we address the limited availability of large annotated databases for real-life audio event detection by utilizing the concept of transfer learning. This technique aims to transfer knowledge from a source domain to a target domain, even if source and target have different feature distributions and label sets. We hypothesize that all acoustic events share the same inventory of basic acoustic building blocks and differ only in the temporal order of these acoustic units. We then construct a deep neural network with convolutional layers for extracting the acoustic units and a recurrent layer for capturing the temporal order. Under the above hypothesis, transfer learning from a source to a target domain with a different acoustic event inventory is realized by transferring the convolutional layers from the source to the target domain. The recurrent layer is, however, learnt directly from the target domain. Experiments on the transfer from a synthetic source database to the reallife target database of DCASE 2016 demonstrate that transfer learning leads to improved detection performance on average. However, the successful transfer to detect events which are very different from what was seen in the source domain, could not be verified.}}, author = {{Arora, Prerna and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)}}, title = {{{A Study on Transfer Learning for Acoustic Event Detection in a Real Life Scenario}}}, year = {{2017}}, } @techreport{11735, abstract = {{This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic operations the derivative of the eigenvalue problem with complex-valued eigenvectors is one of the key results of this report. The potential of this approach is shown with experimental results on the CHiME-3 challenge database. There, the beamforming task is used as a front-end for an ASR system. With the developed derivatives a joint optimization of a speech enhancement and speech recognition system w.r.t. the recognition optimization criterion is possible.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, title = {{{On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming}}}, year = {{2017}}, } @inproceedings{11736, abstract = {{In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural network which provides the clean speech and noise masks from which the beamformer coefficients are estimated by eigenvalue decomposition. A key theoretical result is the derivative of an eigenvalue problem involving complex-valued eigenvectors. Experimental results on the CHiME-3 challenge database demonstrate the effectiveness of the approach. The tools developed in this paper are a key component for an end-to-end optimization of speech enhancement and speech recognition.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation}}}, year = {{2017}}, } @inproceedings{11737, abstract = {{The benefits of both a logarithmic spectral amplitude (LSA) estimation and a modeling in a generalized spectral domain (where short-time amplitudes are raised to a generalized power exponent, not restricted to magnitude or power spectrum) are combined in this contribution to achieve a better tradeoff between speech quality and noise suppression in single-channel speech enhancement. A novel gain function is derived to enhance the logarithmic generalized spectral amplitudes of noisy speech. Experiments on the CHiME-3 dataset show that it outperforms the famous minimum mean squared error (MMSE) LSA gain function of Ephraim and Malah in terms of noise suppression by 1.4 dB, while the good speech quality of the MMSE-LSA estimator is maintained.}}, author = {{Chinaev, Alleksej and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{A Generalized Log-Spectral Amplitude Estimator for Single-Channel Speech Enhancement}}}, year = {{2017}}, } @inproceedings{11754, abstract = {{Recent advances in discriminatively trained mask estimation networks to extract a single source utilizing beamforming techniques demonstrate, that the integration of statistical models and deep neural networks (DNNs) are a promising approach for robust automatic speech recognition (ASR) applications. In this contribution we demonstrate how discriminatively trained embeddings on spectral features can be tightly integrated into statistical model-based source separation to separate and transcribe overlapping speech. Good generalization to unseen spatial configurations is achieved by estimating a statistical model at test time, while still leveraging discriminative training of deep clustering embeddings on a separate training set. We formulate an expectation maximization (EM) algorithm which jointly estimates a model for deep clustering embeddings and complex-valued spatial observations in the short time Fourier transform (STFT) domain at test time. Extensive simulations confirm, that the integrated model outperforms (a) a deep clustering model with a subsequent beamforming step and (b) an EM-based model with a beamforming step alone in terms of signal to distortion ratio (SDR) and perceptually motivated metric (PESQ) gains. ASR results on a reverberated dataset further show, that the aforementioned gains translate to reduced word error rates (WERs) even in reverberant environments.}}, author = {{Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings}}}, year = {{2017}}, } @inproceedings{11770, abstract = {{In this contribution we show how to exploit text data to support word discovery from audio input in an underresourced target language. Given audio, of which a certain amount is transcribed at the word level, and additional unrelated text data, the approach is able to learn a probabilistic mapping from acoustic units to characters and utilize it to segment the audio data into words without the need of a pronunciation dictionary. This is achieved by three components: an unsupervised acoustic unit discovery system, a supervisedly trained acoustic unit-to-grapheme converter, and a word discovery system, which is initialized with a language model trained on the text data. Experiments for multiple setups show that the initialization of the language model with text data improves the word segementation performance by a large margin.}}, author = {{Glarner, Thomas and Boenninghoff, Benedikt and Walter, Oliver and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Leveraging Text Data for Word Segmentation for Underresourced Languages}}}, year = {{2017}}, } @inproceedings{11809, abstract = {{This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from the acoustic model all the way through feature extraction and the complex valued beamforming operation. Besides avoiding a mismatch between the front-end and the back-end, this approach also eliminates the need for stereo data, i.e., the parallel availability of clean and noisy versions of the signals. Instead, it can be trained with real noisy multichannel data only. Also, relying on the signal statistics for beamforming, the approach makes no assumptions on the configuration of the microphone array. We further observe a performance gain through joint training in terms of word error rate in an evaluation of the system on the CHiME 4 dataset.}}, author = {{Heymann, Jahn and Drude, Lukas and Boeddeker, Christoph and Hanebrink, Patrick and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System}}}, year = {{2017}}, } @article{11811, abstract = {{Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition (ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multi-channel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3 challenge, which features challenging real-world noise distortions, and the Reverb challenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, journal = {{Computer Speech and Language}}, title = {{{A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing}}}, year = {{2017}}, } @misc{12081, abstract = {{The invention relates to a building or enclosure termination opening and/or closing apparatus having communication signed or encrypted by means of a key, and to a method for operating such. To allow simple, convenient and secure use by exclusively authorised users, the apparatus comprises: a first and a second user terminal, with secure forwarding of a time-limited key from the first to the second user terminal being possible. According to an alternative, individual keys are generated by a user identification and a secret device key.}}, author = {{Jacob, Florian and Schmalenstroeer, Joerg}}, title = {{{Building or Enclosure Termination Closing and/or Opening Apparatus, and Method for Operating a Building or Enclosure Termination}}}, year = {{2017}}, } @inproceedings{12973, author = {{Deshmukh, Jyotirmoy and Kunz, Wolfgang and Wunderlich, Hans-Joachim and Hellebrand, Sybille}}, booktitle = {{35th IEEE VLSI Test Symposium (VTS'17)}}, publisher = {{IEEE}}, title = {{{Special Session on Early Life Failures}}}, doi = {{10.1109/vts.2017.7928933}}, year = {{2017}}, } @misc{14862, author = {{Webersen, Manuel and Henning, Bernd}}, title = {{{Ultraschallbasierte Charakterisierung des Alterungsverhaltens von Polymeren}}}, year = {{2017}}, } @inproceedings{13861, author = {{Olfert, Sergei and Becker, Sebastian and Henning, Bernd}}, booktitle = {{Deutsche Gesellschaft für Akustik e.V. 2017- Fortschritte der Akustik - DAGA 2017}}, isbn = {{9783939296126}}, pages = {{1015--1018}}, publisher = {{Deutsche Gesellschaft für Akustik e.V. (DEGA)}}, title = {{{Erweiterung des Mason-Modells zur Beschreibung eines Partikelbelags auf einer Quarzscheibe}}}, year = {{2017}}, } @inproceedings{29889, author = {{Peter, Klaus and Mink, Fabian and Böcker, Joachim}}, booktitle = {{2017 IEEE International Electric Machines and Drives Conference (IEMDC)}}, location = {{ Miami, FL, USA}}, publisher = {{IEEE}}, title = {{{Model-based control structure for high-speed permanent magnet synchronous drives}}}, doi = {{10.1109/iemdc.2017.8002284}}, year = {{2017}}, } @inproceedings{21248, author = {{Wallscheid, Oliver and Kirchgässner, Wilhelm and Böcker, Joachim}}, booktitle = {{2017 International Joint Conference on Neural Networks (IJCNN)}}, isbn = {{9781509061822}}, title = {{{Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors}}}, doi = {{10.1109/ijcnn.2017.7966088}}, year = {{2017}}, }