PinSim: Schnelle Simulation mit Pintools - Ikonomakis, Nikolaos

MPI-CUDA Codegenerierung für Nanophoton Simulationen auf Clustern - Kassner, Hendrik

Multi-objective Intrinsic Evolution of Embedded Systems - Kaufmann, Paul; Platzner, Marco (Organic Computing---A Paradigm Shift for Complex Systems)

Analysis of Algorithmic Approaches for Temporal Partitioning - Schwabe, Arne

Evolution of Electronic Circuits - Sekanina, Lukas; Walker, James Alfred; Kaufmann, Paul; Plessl, Christian; Platzner, Marco (Cartesian Genetic Programming)

Problem Decomposition in Cartesian Genetic Programming - Walker, James Alfred; Miller, Julian F.; Kaufmann, Paul; Platzner, Marco (Cartesian Genetic Programming)

User Space Scheduling for Heterogeneous Systems - Welp, Daniel

Parallele Kompression von XML mit XML-Schema-Subtraktion - Feldotto, Matthias

A Platform for efficient Supply Chain Management Support in Logistics - Bevermeier, Maik; Flanke, Stephan; Haeb-Umbach, Reinhold; Stehr, Jan (International Workshop on Intelligent Transportation (WIT 2011))

Uncertainty Decoding and Conditional Bayesian Estimation - Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
In this contribution classification rules for HMM-based speech recognition in the presence of a mismatch between training and test data are presented. The observed feature vectors are regarded as corrupted versions of underlying and unobservable clean feature vectors, which have the same statistics as the training data. Optimal classification then consists of two steps. First, the posterior density of the clean feature vector, given the observed feature vectors, has to be determined, and second, this posterior is employed in a modified classification rule, which accounts for imperfect estimates. We discuss different variants of the classification rule and further elaborate on the estimation of the clean speech feature posterior, using conditional Bayesian estimation. It is shown that this concept is fairly general and can be applied to different scenarios, such as noisy or reverberant speech recognition.

Können Computer sprechen und hören, sollen sie es überhaupt können? Sprachverarbeitung und ambiente Intelligenz - Haeb-Umbach, Reinhold (Baustelle Informationsgesellschaft und Universität heute)

Adaptive Systems for Unsupervised Speaker Tracking and Speech Recognition - Herbig, Tobias; Gerl, Franz; Minker, Wolfgang; Haeb-Umbach, Reinhold (Evolving Systems)

A Model-Based Approach to Joint Compensation of Noise and Reverberation for Speech Recognition - Krueger, Alexander; Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
Employing automatic speech recognition systems in hands-free communication applications is accompanied by perfomance degradation due to background noise and, in particular, due to reverberation. These two kinds of distortion alter the shape of the feature vector trajectory extracted from the microphone signal and consequently lead to a discrepancy between training and testing conditions for the recognizer. In this chapter we present a feature enhancement approach aiming at the joint compensation of noise and reverberation to improve the performance by restoring the training conditions. For the enhancement we concentrate on the logarithmic mel power spectral coefficients as features, which are computed at an intermediate stage to obtain the widely used mel frequency cepstral coefficients. The proposed technique is based on a Bayesian framework, to attempt to infer the posterior distribution of the clean features given the observation of all past corrupted features. It exploits information from a priori models describing the dynamics of clean speech and noise-only feature vector trajectories as well as from an observation model relating the reverberant noisy to the clean features. The observation model relies on a simplified stochastic model of the room impulse response (RIR) between the speaker and the microphone, having only two parameters, namely RIR energy and reverberation time, which can be estimated from the captured microphone signal. The performance of the proposed enhancement technique is finally experimentally studied by means of recognition accuracy obtained for a connected digits recognition task under different noise and reverberation conditions using the Aurora~5 database.

MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations - Krueger, Alexander; Haeb-Umbach, Reinhold (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011))
The paper proposes a modification of the standard maximum a posteriori (MAP) method for the estimation of the parameters of a Gaussian process for cases where the process is superposed by additive Gaussian observation errors of known variance. Simulations on artificially generated data demonstrate the superiority of the proposed method. While reducing to the ordinary MAP approach in the absence of observation noise, the improvement becomes the more pronounced the larger the variance of the observation noise. The method is further extended to track the parameters in case of non-stationary Gaussian processes.
Keywords: Gaussian processes; MAP-based estimation; maximum a posteriori method; maximum likelihood estimation; nonstationary Gaussian processes

Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation - Krueger, Alexander; Warsitz, Ernst; Haeb-Umbach, Reinhold (IEEE Transactions on Audio, Speech, and Language Processing)
In this paper, we present a novel blocking matrix and fixed beamformer design for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure. They are based on a new method for estimating the acoustical transfer function ratios in the presence of stationary noise. The estimation method relies on solving a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector tracking utilizing the power iteration method is employed and shown to achieve a high convergence speed. Simulation results demonstrate that the proposed beamformer leads to better noise and interference reduction and reduced speech distortions compared to other blocking matrix designs from the literature.
Keywords: acoustical transfer function ratio; adaptive eigenvector tracking; array signal processing; beamformer design; blocking matrix; eigenvalues and eigenfunctions; eigenvector-based transfer function ratios estimation; generalized sidelobe canceler; interference reduction; iterative methods; power iteration method; reduced speech distortions; reverberant enclosure; reverberation; speech enhancement; stationary noise

Conditional Bayesian Estimation Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition - Leutnant, Volker; Haeb-Umbach, Reinhold (Robust Speech Recognition of Uncertain or Missing Data)
In this contribution, conditional Bayesian estimation employing a phase-sensitive observation model for noise robust speech recognition will be studied. After a review of speech recognition under the presence of corrupted features, termed uncertainty decoding, the estimation of the posterior distribution of the uncorrupted (clean) feature vector will be shown to be a key element of noise robust speech recognition. The estimation process will be based on three major components: an a priori model of the unobservable data, an observation model relating the unobservable data to the corrupted observation and an inference algorithm, finally allowing for a computationally tractable solution. Special stress will be laid on a detailed derivation of the phase-sensitive observation model and the required moments of the phase factor distribution. Thereby, it will not only be proven analytically that the phase factor distribution is non-Gaussian but also that all central moments can (approximately) be computed solely based on the used mel filter bank, finally rendering the moments independent of noise type and signal-to-noise ratio. The phase-sensitive observation model will then be incorporated into a model-based feature enhancement scheme and recognition experiments will be carried out on the Aurora~2 and Aurora~4 databases. The importance of incorporating phase factor information into the enhancement scheme is pointed out by all recognition results. Application of the proposed scheme under the derived uncertainty decoding framework further leads to significant improvements in both recognition tasks, eventually reaching the performance achieved with the ETSI advanced front-end.

A versatile Gaussian splitting approach to non-linear state estimation and its application to noise-robust ASR - Leutnant, Volker; Krueger, Alexander; Haeb-Umbach, Reinhold (Interspeech 2011)
In this work, a splitting and weighting scheme that allows for splitting a Gaussian density into a Gaussian mixture density (GMM) is extended to allow the mixture components to be arranged along arbitrary directions. The parameters of the Gaussian mixture are chosen such that the GMM and the original Gaussian still exhibit equal central moments up to an order of four. The resulting mixtures{\rq} covariances will have eigenvalues that are smaller than those of the covariance of the original distribution, which is a desirable property in the context of non-linear state estimation, since the underlying assumptions of the extended K ALMAN filter are better justified in this case. Application to speech feature enhancement in the context of noise-robust automatic speech recognition reveals the beneficial properties of the proposed approach in terms of a reduced word error rate on the Aurora 2 recognition task.

On Initial Seed Selection for Frequency Domain Blind Speech Separation - Tran Vu, Dang Hai; Haeb-Umbach, Reinhold (Interspeech 2011)
In this paper we address the problem of initial seed selection for frequency domain iterative blind speech separation (BSS) algorithms. The derivation of the seeding algorithm is guided by the goal to select samples which are likely to be caused by source activity and not by noise and at the same time originate from different sources. The proposed algorithm has moderate computational complexity and finds better seed values than alternative schemes, as is demonstrated by experiments on the database of the SiSEC2010 challenge.

Robust Speech Recognition of Uncertain or Missing Data --- Theory and Applications

Performance Evaluation of Network Mobility Handover over Future Aeronautical Data Link - Ayaz, Serkan; Hoffmann, Felix; Sommer, Christoph; German, Reinhard; Dressler, Falko (2010 IEEE Global Telecommunications Conference GLOBECOM 2010)