@inproceedings{11873, abstract = {{NARA-WPE is a Python software package providing implementations of the weighted prediction error (WPE) dereverberation algorithm. WPE has been shown to be a highly effective tool for speech dereverberation, thus improving the perceptual quality of the signal and improving the recognition performance of downstream automatic speech recognition (ASR). It is suitable both for single-channel and multi-channel applications. The package consist of (1) a Numpy implementation which can easily be integrated into a custom Python toolchain, and (2) a TensorFlow implementation which allows integration into larger computational graphs and enables backpropagation through WPE to train more advanced front-ends. This package comprises of an iterative offline (batch) version, a block-online version, and a frame-online version which can be used in moderately low latency applications, e.g. digital speech assistants.}}, author = {{Drude, Lukas and Heymann, Jahn and Boeddeker, Christoph and Haeb-Umbach, Reinhold}}, booktitle = {{ITG 2018, Oldenburg, Germany}}, title = {{{NARA-WPE: A Python package for weighted prediction error dereverberation in Numpy and Tensorflow for online and offline processing}}}, year = {{2018}}, } @article{11916, abstract = {{We present an experimental comparison of seven state-of-the-art machine learning algorithms for the task of semantic analysis of spoken input, with a special emphasis on applications for dysarthric speech. Dysarthria is a motor speech disorder, which is characterized by poor articulation of phonemes. In order to cater for these noncanonical phoneme realizations, we employed an unsupervised learning approach to estimate the acoustic models for speech recognition, which does not require a literal transcription of the training data. Even for the subsequent task of semantic analysis, only weak supervision is employed, whereby the training utterance is accompanied by a semantic label only, rather than a literal transcription. Results on two databases, one of them containing dysarthric speech, are presented showing that Markov logic networks and conditional random fields substantially outperform other machine learning approaches. Markov logic networks have proved to be especially robust to recognition errors, which are caused by imprecise articulation in dysarthric speech.}}, author = {{Despotovic, Vladimir and Walter, Oliver and Haeb-Umbach, Reinhold}}, journal = {{Speech Communication 99 (2018) 242-251 (Elsevier B.V.)}}, title = {{{Machine learning techniques for semantic analysis of dysarthric speech: An experimental study}}}, year = {{2018}}, } @inproceedings{12898, abstract = {{Deep clustering (DC) and deep attractor networks (DANs) are a data-driven way to monaural blind source separation. Both approaches provide astonishing single channel performance but have not yet been generalized to block-online processing. When separating speech in a continuous stream with a block-online algorithm, it needs to be determined in each block which of the output streams belongs to whom. In this contribution we solve this block permutation problem by introducing an additional speaker identification embedding to the DAN model structure. We motivate this model decision by analyzing the embedding topology of DC and DANs and show, that DC and DANs themselves are not sufficient for speaker identification. This model structure (a) improves the signal to distortion ratio (SDR) over a DAN baseline and (b) provides up to 61% and up to 34% relative reduction in permutation error rate and re-identification error rate compared to an i-vector baseline, respectively.}}, author = {{Drude, Lukas and von Neumann, Thilo and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2018, Calgary, Canada}}, title = {{{Deep Attractor Networks for Speaker Re-Identifikation and Blind Source Separation}}}, year = {{2018}}, } @inproceedings{12900, abstract = {{Deep attractor networks (DANs) are a recently introduced method to blindly separate sources from spectral features of a monaural recording using bidirectional long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is inherently not online-ready and resorting to operating on blocks yields a block permutation problem in that the index of each speaker may change between blocks. We here propose the joint modeling of spatial and spectral features to solve the block permutation problem and generalize DANs to multi-channel meeting recordings: The DAN acts as a spectral feature extractor for a subsequent model-based clustering approach. We first analyze different joint models in batch-processing scenarios and finally propose a block-online blind source separation algorithm. The efficacy of the proposed models is demonstrated on reverberant mixtures corrupted by real recordings of multi-channel background noise. We demonstrate that both the proposed batch-processing and the proposed block-online system outperform (a) a spatial-only model with a state-of-the-art frequency permutation solver and (b) a spectral-only model with an oracle block permutation solver in terms of signal to distortion ratio (SDR) gains.}}, author = {{Drude, Lukas and Higuchi,, Takuya and Kinoshita, Keisuke and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2018, Calgary, Canada}}, title = {{{Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source Separation}}}, year = {{2018}}, } @inproceedings{12901, abstract = {{This work examines acoustic beamformers employing neural networks (NNs) for mask prediction as front-end for automatic speech recognition (ASR) systems for practical scenarios like voice-enabled home devices. To test the versatility of the mask predicting network, the system is evaluated with different recording hardware, different microphone array designs, and different acoustic models of the downstream ASR system. Significant gains in recognition accuracy are obtained in all configurations despite the fact that the NN had been trained on mismatched data. Unlike previous work, the NN is trained on a feature level objective, which gives some performance advantage over a mask related criterion. Furthermore, different approaches for realizing online, or adaptive, NN-based beamforming are explored, where the online algorithms still show significant gains compared to the baseline performance.}}, author = {{Boeddeker, Christoph and Erdogan, Hakan and Yoshioka, Takuya and Haeb-Umbach, Reinhold}}, booktitle = {{ICASSP 2018, Calgary, Canada}}, title = {{{Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition}}}, year = {{2018}}, } @article{12974, author = {{Hellebrand, Sybille and Henkel, Joerg and Raghunathan, Anand and Wunderlich, Hans-Joachim}}, journal = {{IEEE Embedded Systems Letters}}, number = {{1}}, pages = {{1--1}}, publisher = {{IEEE}}, title = {{{Guest Editors' Introduction - Special Issue on Approximate Computing}}}, doi = {{10.1109/les.2018.2789942}}, volume = {{10}}, year = {{2018}}, } @article{15845, author = {{Ameli, Ali and Krauter, Stefan and Ameli, Mohammad Taghi and Moslehpour, Saeid}}, issn = {{2152 4157}}, journal = {{International Journal of Engineering Research & Innovation}}, number = {{1}}, title = {{{Smart charging management system of plugged-in EVs based on user driving patterns in micro-grids}}}, volume = {{10}}, year = {{2018}}, } @article{1430, author = {{Hoffmann, Sandro P. and Albert, Maximilian and Weber, Nils and Sievers, Denis and Förstner, Jens and Zentgraf, Thomas and Meier, Cedrik}}, issn = {{2330-4022}}, journal = {{ACS Photonics}}, keywords = {{tet_topic_phc}}, pages = {{1933--1942}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Tailored UV Emission by Nonlinear IR Excitation from ZnO Photonic Crystal Nanocavities}}}, doi = {{10.1021/acsphotonics.7b01228}}, volume = {{5}}, year = {{2018}}, } @article{13057, author = {{Kampmann, Matthias and Hellebrand, Sybille}}, journal = {{Microelectronics Reliability}}, pages = {{124--133}}, title = {{{Design For Small Delay Test - A Simulation Study}}}, volume = {{80}}, year = {{2018}}, } @misc{13072, author = {{Kampmann, Matthias and Hellebrand, Sybille}}, keywords = {{WORKSHOP}}, title = {{{Optimized Constraints for Scan-Chain Insertion for Faster-than-at-Speed Test}}}, year = {{2018}}, } @inproceedings{29888, author = {{Buchholz, Oleg and Böcker, Joachim}}, booktitle = {{2018 IEEE 27th International Symposium on Industrial Electronics (ISIE)}}, location = {{Cairns, QLD, Australia}}, publisher = {{IEEE}}, title = {{{Online-Identification of the Machine Parameters of an Induction Motor Drive}}}, doi = {{10.1109/isie.2018.8433852}}, year = {{2018}}, } @inproceedings{29912, author = {{Buchholz, Oleg and Böcker, Joachim}}, booktitle = {{2017 IEEE Southern Power Electronics Conference (SPEC)}}, location = {{ Puerto Varas, Chile}}, publisher = {{IEEE}}, title = {{{Gopinath-observer for flux estimation of an induction machine drive system}}}, doi = {{10.1109/spec.2017.8333614}}, year = {{2018}}, } @inproceedings{29911, author = {{Vogt, Thorsten and Badeda, Julia and Böcker, Joachim and Sauer, Dirk Uwe}}, booktitle = {{2017 IEEE 12th International Conference on Power Electronics and Drive Systems (PEDS)}}, location = {{Honolulu, HI, USA }}, publisher = {{IEEE}}, title = {{{Consideration on primary control reserve provision by industrial microgrids in grid-coupled operation}}}, doi = {{10.1109/peds.2017.8289189}}, year = {{2018}}, } @inproceedings{29905, author = {{Joy, Meryl Teresa and Böcker, Joachim}}, booktitle = {{2018 IEEE 9th International Symposium on Sensorless Control for Electrical Drives (SLED)}}, location = {{Helsinki, Finland }}, publisher = {{IEEE}}, title = {{{Sensorless Control of Induction Motor Drives Using Additional Windings on the Stator}}}, doi = {{10.1109/sled.2018.8486038}}, year = {{2018}}, } @inproceedings{29908, author = {{Bolte, Sven and Fröhleke, Norbert and Böcker, Joachim}}, booktitle = {{PCIM Europe 2018; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management}}, location = {{ Nuremberg, Germany}}, pages = {{1--6}}, title = {{{GaN Buck Converter in CCM with Optimized High Frequency Inductors}}}, year = {{2018}}, } @inproceedings{29943, author = {{Weber, Daniel and Stille, Karl Stephan Christian and Wallscheid, Oliver and Böcker, Joachim}}, booktitle = {{2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM)}}, publisher = {{IEEE}}, title = {{{Energy Management for a Nano-CHP Unit and an Electrical Storage System in a Residential Application}}}, doi = {{10.1109/speedam.2018.8445296}}, year = {{2018}}, } @inproceedings{29628, author = {{Wallscheid, Oliver and Schenke, Maximilian and Böcker, Joachim}}, booktitle = {{2018 21st International Conference on Electrical Machines and Systems (ICEMS)}}, pages = {{1181–1186}}, title = {{{Improving torque and speed estimation accuracy by conjoint parameter identification and unscented Kalman filter design for induction machines}}}, year = {{2018}}, } @inproceedings{29625, author = {{Wallscheid, Oliver and Schenke, Maximilian and Böcker, Joachim}}, booktitle = {{2018 IEEE 18th International Power Electronics and Motion Control Conference (PEMC)}}, pages = {{793–799}}, title = {{{A combined approach to identify induction machine parameters and to design an extended kalman filter for speed and torque estimation}}}, year = {{2018}}, } @inproceedings{29957, author = {{Buchholz, Oleg and Böcker, Joachim and Bonifacio, João}}, booktitle = {{2018 XIII International Conference on Electrical Machines (ICEM)}}, location = {{Alexandroupoli, Greece}}, publisher = {{IEEE}}, title = {{{Online-Identification of the Induction Machine Parameters Using the Extended Kalman Filter}}}, doi = {{10.1109/icelmach.2018.8506739}}, year = {{2018}}, } @inproceedings{29887, author = {{Joy, Meryl Teresa and Böcker, Joachim}}, booktitle = {{2018 20th European Conference on Power Electronics and Applications (EPE’18 ECCE Europe)}}, location = {{Riga, Lettland}}, pages = {{P.1--P.10}}, title = {{{Speed Estimation in Induction Motors Using Additional Windings}}}, year = {{2018}}, }