On the appropriateness of complex-valued neural networks for speech enhancement - Drude, Lukas; Raj, Bhiksha; Haeb-Umbach, Reinhold (INTERSPEECH 2016, San Francisco, USA)
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.

Factor Graph Decoding for Speech Presence Probability Estimation - Glarner, Thomas; Mahdi Momenzadeh, Mohammad; Drude, Lukas; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
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.

Neural Network Based Spectral Mask Estimation for Acoustic Beamforming - Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold (Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP))

On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays - Jacob, Florian; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
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.

Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition - Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold (Computer Speech and Language)
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.

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research - Kinoshita, Keisuke; Delcroix, Marc; Gannot, Sharon; Habets, Emanuel A. P.; Haeb-Umbach, Reinhold; Kellermann, Walter; Leutnant, Volker; Maas, Roland; Nakatani, Tomohiro; Raj, Bhiksha; Sehr, Armin; Yoshioka, Takuya (EURASIP Journal on Advances in Signal Processing)

Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms - Plinge, Axel; Jacob, Florian; Haeb-Umbach, Reinhold; Fink, Gernot A. (IEEE Signal Processing Magazine)
Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.
Keywords: Acoustic sensors; Microphones; Portable computers; Smart phones; Wireless communication; Wireless sensor networks

Induction-based Verification of Timed Systems - Isenberg, Tobias

The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation - Menne, Tobias; Heymann, Jahn; Alexandridis, Anastasios; Irie, Kazuki; Zeyer, Albert; Kitza, Markus; Golik, Pavel; Kulikov, Ilia; Drude, Lukas; Schlüter, Ralf; Ney, Hermann; Haeb-Umbach, Reinhold; et al. (Computer Speech and Language)
This paper describes automatic speech recognition (ASR) systems developed jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME Challenge. In the 2ch and 6ch tracks the final system output is obtained by a Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM) is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM) units. The systems differ by front ends and training sets used for the acoustic training. The model for the 1ch track is trained without any preprocessing. For each front end we trained and evaluated individual acoustic models. We compare the ASR performance of different beamforming approaches: a conventional superdirective beamformer [1] and an MVDR beamformer as in [2], where the steering vector is estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices with a Long Short-Term Memory (LSTM) based language model. The overall best results are obtained by a system combination that includes the lattices from the system of UPB?s submission [8]. Our final submission scored second in each of the three tracks of the 4th CHiME Challenge.

Unsupervised Word Discovery from Speech using Bayesian Hierarchical Models - Walter, Oliver; Haeb-Umbach, Reinhold (38th German Conference on Pattern Recognition (GCPR 2016))
In this paper we demonstrate an algorithm to learn words from speech using non-parametric Bayesian hierarchical models in an unsupervised setting. We exploit the assumption of a hierarchical structure of speech, namely the formation of spoken words as a sequence of phonemes. We employ the Nested Hierarchical Pitman-Yor Language Model, which allows an a priori unknown and possibly unlimited number of words. We assume the n-gram probabilities of words, the m-gram probabilities of phoneme sequences in words and the phoneme sequences of the words themselves as latent variables to be learned. We evaluate the algorithm on a cross language task using an existing speech recognizer trained on English speech to decode speech in the Xitsonga language supplied for the 2015 ZeroSpeech challenge. We apply the learning algorithm on the resulting phoneme graphs and achieve the highest token precision and F score compared to present systems.

Protocol options for low power sensor network MAC using wake-up receivers with duty cycling - Blobel, Johannes; Sommer, Christoph; Dressler, Falko (2016 IEEE International Conference on Communications (ICC))

On the impact of adjacent channel interference in multi-channel VANETs - Campolo, Claudia; Sommer, Christoph; Dressler, Falko; Molinaro, Antonella (2016 IEEE International Conference on Communications (ICC))

Cars as the base for service discovery and provision in highly dynamic networks - Hagenauer, Florian; Sommer, Christoph; Merschjohann, Simon; Higuchi, Takamasa; Dressler, Falko; Altintas, Onur (2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

Interconnecting smart cities by vehicles: How feasible is it? - Hagenauer, Florian; Sommer, Christoph; Onishi, Ryokichi; Wilhelm, Matthias; Dressler, Falko; Altintas, Onur (2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

Cars as a main ICT resource of smart cities - Hagenauer, F.; Dressler, F.; Altintas, O.; Sommer, Christoph (Smart Cities and Homes)

IEEE 802.11p unicast considered harmful - Klingler, Florian; Dressler, Falko; Sommer, Christoph (2015 IEEE Vehicular Networking Conference (VNC))

Cluster-based transmit power control in heterogeneous vehicular networks - Memedi, Agon; Hagenauer, Florian; Dressler, Falko; Sommer, Christoph (2015 IEEE Vehicular Networking Conference (VNC))

Self-aware Computing Systems: An Engineering Approach
Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions. This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.

X Marks the Spot: Scan-Flip-Flop Clustering for Faster-than-at-Speed Test - Kampmann, Matthias; Hellebrand, Sybille (25th IEEE Asian Test Symposium (ATS'16))

Selfish Network Creation - On Variants of Network Creation Games - Cord-Landwehr, Andreas