TY - CONF AB - State estimation when only a partial model of a considered system is available remains a major challenge in many engineering fields. This work proposes a joint, square-root unscented Kalman filter to estimate states and model uncertainties simultaneously by linear combinations of physics-motivated library functions. Using a sparsity promoting approach, a selection of those linear combinations is chosen and thus an interpretable model can be extracted. Results indicate a small estimation error compared to a traditional square-root unscented Kalman filter and exhibit the enhancement of physically meaningful models. AU - Götte, Ricarda-Samantha AU - Timmermann, Julia ID - 34171 IS - 1 KW - joint estimation KW - unscented transform KW - Kalman filter KW - sparsity KW - data-driven KW - compressed sensing T2 - 12th IFAC Symposium on Nonlinear Control Systems (NOLCOS 2022) TI - Estimating States and Model Uncertainties Jointly by a Sparsity Promoting UKF VL - 56 ER - TY - CONF AB - Low-quality models that miss relevant dynamics lead to major challenges in modelbased state estimation. We address this issue by simultaneously estimating the system’s states and its model inaccuracies by a square root unscented Kalman filter (SRUKF). Concretely, we augment the state with the parameter vector of a linear combination containing suitable functions that approximate the lacking dynamics. Presuming that only a few dynamical terms are relevant, the parameter vector is claimed to be sparse. In Bayesian setting, properties like sparsity are expressed by a prior distribution. One common choice for sparsity is a Laplace distribution. However, due to disadvantages of a Laplacian prior in regards to the SRUKF, the regularized horseshoe distribution, a Gaussian that approximately features sparsity, is applied instead. Results exhibit small estimation errors with model improvements detected by an automated model reduction technique. AU - Götte, Ricarda-Samantha AU - Timmermann, Julia ID - 44326 IS - 2 KW - joint estimation KW - unscented Kalman filter KW - sparsity KW - Laplacian prior KW - regularized horseshoe KW - principal component analysis T2 - IFAC-PapersOnLine TI - Approximating a Laplacian Prior for Joint State and Model Estimation within an UKF VL - 56 ER - TY - CHAP AB - Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed. AU - Malena, Kevin AU - Link, Christopher AU - Bußemas, Leon AU - Gausemeier, Sandra AU - Trächtler, Ansgar ED - Klein, Cornel ED - Jarke, Mathias ED - Helfert, Markus ED - Berns, Karsten ED - Gusikhin, Oleg ID - 33849 KW - Traffic control KW - Traffic estimation KW - Real-time KW - MPC KW - Fuzzy KW - Isolated intersection KW - Networked intersection KW - Sensor fusion SN - 1865-0929 T2 - Communications in Computer and Information Science TI - Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments VL - 1612 ER - TY - CONF AB - Access to precise meteorological data is crucial to be able to plan and install renewable energy systems such as solar power plants and wind farms. In case of solar energy, knowledge of local irradiance and air temperature values is very important. For this, various methods can be used such as installing local weather stations or using meteorological data from different organizations such as Meteonorm or official Deutscher Wetterdienst (DWD). An alternative is to use satellite reanalysis datasets provided by organizations like the National Aeronautics and Space Administration (NASA) and European Centre for Medium-Range Weather Forecasts (ECMWF). In this paper the “Modern-Era Retrospective analysis for Research and Applications” dataset version 2 (MERRA-2) will be presented, and its performance will be evaluated by comparing it to locally measured datasets provided by Meteonorm and DWD. The analysis shows very high correlation between MERRA-2 and local measurements (correlation coefficients of 0.99) for monthly global irradiance and air temperature values. The results prove the suitability of MERRA-2 data for applications requiring long historical data. Moreover, availability of MERRA-2 for the whole world with an acceptable resolution makes it a very valuable dataset. AU - Khatibi, Arash AU - Krauter, Stefan ID - 24551 KW - Energy potential estimation KW - Photovoltaic KW - Solar radiation KW - Temperature measurement KW - Satellite data KW - Meteonorm KW - MERRA-2 KW - DWD SN - 3-936338-78-7 T2 - Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (EUPVSEC 2021) TI - Comparison and Validation of Irradiance Data: Satellite Meteorological Dataset MERRA-2 vs. Meteonorm and German Weather Service (DWD) ER - TY - CONF AB - The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic area can be challenging depending on the provided data. This paper presents a novel method based on limited data from sensors positioned at specific locations and guarantees a general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual purpose of research is of particular importance. Here, the research aims at improving the traffic flow by controlling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and the route choices of individual road users are the matter of interest. An integer optimization problem is derived to fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic network and results in an observation of the current multimodal traffic state matching at the given sensor position. First case studies show promosing results in terms of deviations between reality and simulation. AU - Malena, Kevin AU - Link, Christopher AU - Mertin, Sven AU - Gausemeier, Sandra AU - Trächtler, Ansgar ID - 24159 KW - Microscopic Traffic Simulation KW - Online State Estimation KW - Mixed Road Users KW - Sensor Fusion KW - Integer Programming KW - Route Choice KW - Vehicle2Infrastructure SN - 978-989-758-513-5 T2 - VEHITS 2021 Proceedings of the 7th International Conference on Vehicle Technology and Intelligent Transport Systems TI - Online State Estimation for Microscopic Traffic Simulations using Multiple Data Sources* VL - 7 ER - TY - CONF AB - Predictive Maintenance as a desirable maintenance strategy in industrial applications relies on suitable condition monitoring solutions to reduce costs and risks of the monitored technical systems. In general, those solutions utilize model-based or data-driven methods to diagnose the current state or predict future states of monitored technical systems. However, both methods have their advantages and drawbacks. Combining both methods can improve uncertainty consideration and accuracy. Different combination approaches of those hybrid methods exist to exploit synergy effects. The choice of an appropriate approach depends on different requirements and the goal behind the selection of a hybrid approach. In this work, the hybrid approach for estimating remaining useful lifetime takes potential uncertainties into account. Therefore, a data-driven estimation of new measurements is integrated within a model-based method. To consider uncertainties within the system, a differentiation between different system behavior is realized throughout diverse states of degradation. The developed hybrid prediction approach bases on a particle filtering method combined with a machine learning method, to estimate the remaining useful lifetime of technical systems. Particle filtering as a Monte Carlo simulation technique is suitable to map and propagate uncertainties. Moreover, it is a state-of-the-art model-based method for predicting remaining useful lifetime of technical systems. To integrate uncertainties a multi-model particle filtering approach is employed. In general, resampling as a part of the particle filtering approach has the potential to lead to an accurate prediction. However, in the case where no future measurements are available, it may increase the uncertainty of the prediction. By estimating new measurements, those uncertainties are reduced within the data-driven part of the approach. Hence, both parts of the hybrid approach strive to account for and reduce uncertainties. Rubber-metal-elements are employed as a use-case to evaluate the developed approach. Rubber-metal-elements, which are used to isolate vibrations in various systems, such as railways, trucks and wind turbines, show various uncertainties in their behavior and their degradation. Those uncertainties are caused by diverse inner and outer factors, such as manufacturing influences and operating conditions. By expert knowledge the influences are described, analyzed and if possible reduced. However, the remaining uncertainties are considered within the hybrid prediction method. Relative temperature is the selected measurand to describe the element’s degradation. In lifetime tests, it is measured as the difference between the element’s temperature and the ambient temperature. Thereby, the influence of the ambient temperature on the element’s temperature is taken into account. Those elements show three typical states of degradation that are identified within the temperature measurements. Depending on the particular state of degradation a new measurement is estimated within the hybrid approach to reduce potential uncertainties. Finally, the performance of the developed hybrid method is compared to a model-based method for estimating the remaining useful lifetime of the same elements. Suitable performance indices are implemented to underline the differences between the results. AU - Bender, Amelie AU - Sextro, Walter ED - Do, Phuc ED - King, Steve ED - Fink, Olga ID - 22724 IS - 1 KW - Hybrid prediction method KW - Multi-model particle filtering KW - Uncertainty quantification KW - RUL estimation T2 - Proceedings of the European Conference of the PHM Society 2021 TI - Hybrid Prediction Method for Remaining Useful Lifetime Estimation Considering Uncertainties VL - 6 ER - TY - CONF AB - Piezoelectric transducers are used in a wide range of applications. Reliability of these transducers is an important aspect in their application. Prognostics, which involve continuous monitoring of the health of technical systems and using this information to estimate the current health state and consequently predict the remaining useful lifetime (RUL), can be used to increase the reliability, safety, and availability of the transducers. This is achieved by utilizing the health state and RUL predictions to adaptively control the usage of the components or to schedule appropriate maintenance without interrupting operation. In this work, a prognostic approach utilizing self-sensing, where electric signals of a piezoelectric transducer are used as the condition monitoring data, is proposed. The approach involves training machine learning algorithms to model the degradation of the transducers through a health index and the use of the learned model to estimate the health index of similar transducers. The current health index is then used to estimate RUL of test components. The feasibility of the approach is demonstrated using piezoelectric bimorphs and the results show that the method is accurate in predicting the health index and RUL. AU - Kimotho, James Kuria AU - Sextro, Walter AU - Hemsel, Tobias ID - 9978 KW - Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing T2 - IEEE Transactions on Reliability TI - Estimation of Remaining Useful Lifetime of Piezoelectric Transducers Based on Self-Sensing ER - TY - CONF AB - Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5\% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge. AU - Kimotho, James Kuria AU - Meyer, Tobias AU - Sextro, Walter ID - 9879 KW - ageing KW - particle filtering (numerical methods) KW - proton exchange membrane fuel cells KW - remaining life assessment KW - PEM fuel cell prognostics KW - PHM KW - RUL predictions KW - accelerated degradation KW - adaptive particle filter algorithm KW - dynamic loading KW - model parameter adaptation KW - prognostics and health management KW - proton exchange membrane fuel cells KW - remaining useful life estimation KW - self-healing effect KW - Adaptation models KW - Data models KW - Degradation KW - Estimation KW - Fuel cells KW - Mathematical model KW - Prognostics and health management T2 - Prognostics and Health Management (PHM), 2014 IEEE Conference on TI - PEM fuel cell prognostics using particle filter with model parameter adaptation ER - TY - CONF AB - This contribution describes a step-wise source counting algorithm to determine the number of speakers in an offline scenario. Each speaker is identified by a variational expectation maximization (VEM) algorithm for complex Watson mixture models and therefore directly yields beamforming vectors for a subsequent speech separation process. An observation selection criterion is proposed which improves the robustness of the source counting in noise. The algorithm is compared to an alternative VEM approach with Gaussian mixture models based on directions of arrival and shown to deliver improved source counting accuracy. The article concludes by extending the offline algorithm towards a low-latency online estimation of the number of active sources from the streaming input data. AU - Drude, Lukas AU - Chinaev, Aleksej AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11753 KW - Accuracy KW - Acoustics KW - Estimation KW - Mathematical model KW - Soruce separation KW - Speech KW - Vectors KW - Bayes methods KW - Blind source separation KW - Directional statistics KW - Number of speakers KW - Speaker diarization T2 - 14th International Workshop on Acoustic Signal Enhancement (IWAENC 2014) TI - Towards Online Source Counting in Speech Mixtures Applying a Variational EM for Complex Watson Mixture Models ER - TY - CONF AB - The accuracy of automatic speech recognition systems in noisy and reverberant environments can be improved notably by exploiting the uncertainty of the estimated speech features using so-called uncertainty-of-observation techniques. In this paper, we introduce a new Bayesian decision rule that can serve as a mathematical framework from which both known and new uncertainty-of-observation techniques can be either derived or approximated. The new decision rule in its direct form leads to the new significance decoding approach for Gaussian mixture models, which results in better performance compared to standard uncertainty-of-observation techniques in different additive and convolutive noise scenarios. AU - Abdelaziz, Ahmed H. AU - Zeiler, Steffen AU - Kolossa, Dorothea AU - Leutnant, Volker AU - Haeb-Umbach, Reinhold ID - 11716 KW - Bayes methods KW - Gaussian processes KW - convolution KW - decision theory KW - decoding KW - noise KW - reverberation KW - speech coding KW - speech recognition KW - Bayesian decision rule KW - GMM KW - Gaussian mixture models KW - additive noise scenarios KW - automatic speech recognition systems KW - convolutive noise scenarios KW - decoding approach KW - mathematical framework KW - reverberant environments KW - significance decoding KW - speech feature estimation KW - uncertainty-of-observation techniques KW - Hidden Markov models KW - Maximum likelihood decoding KW - Noise KW - Speech KW - Speech recognition KW - Uncertainty KW - Uncertainty-of-observation KW - modified imputation KW - noise robust speech recognition KW - significance decoding KW - uncertainty decoding SN - 1520-6149 T2 - Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on TI - GMM-based significance decoding ER - TY - CONF AB - In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold ID - 11740 KW - Gaussian noise KW - maximum likelihood estimation KW - parameter estimation KW - GMM parameter KW - Gaussian mixture model KW - MAP estimation KW - Map-based estimation KW - maximum a-posteriori estimation KW - maximum likelihood technique KW - noisy observation KW - sequential estimation framework KW - white Gaussian noise KW - Additive noise KW - Gaussian mixture model KW - Maximum likelihood estimation KW - Noise measurement KW - Gaussian mixture model KW - Maximum a posteriori estimation KW - Maximum likelihood estimation SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations ER - TY - CONF AB - In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms. AU - Hoang, Manh Kha AU - Haeb-Umbach, Reinhold ID - 11816 KW - Gaussian processes KW - Global Positioning System KW - convergence KW - expectation-maximisation algorithm KW - fingerprint identification KW - indoor radio KW - signal classification KW - wireless LAN KW - EM algorithm KW - ML estimation KW - WiFi indoor positioning KW - censored Gaussian data classification KW - clipped data KW - convergence properties KW - expectation maximization algorithm KW - fingerprinting method KW - maximum likelihood estimation KW - optimal classification KW - parameters estimation KW - portable devices sensitivity KW - signal strength measurements KW - wireless LAN positioning systems KW - Convergence KW - IEEE 802.11 Standards KW - Maximum likelihood estimation KW - Parameter estimation KW - Position measurement KW - Training KW - Indoor positioning KW - censored data KW - expectation maximization KW - signal strength KW - wireless LAN SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) TI - Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning ER - TY - CONF AB - In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm. AU - Vu, Dang Hai Tran AU - Haeb-Umbach, Reinhold ID - 11917 KW - correlation methods KW - estimation theory KW - hidden Markov models KW - iterative methods KW - probability KW - spectral analysis KW - speech processing KW - 2D HMM KW - SPP estimates KW - iterative algorithm KW - posterior probability estimation KW - spectral correlation KW - speech presence probability estimation KW - state-of-the-art SPP estimation algorithm KW - temporal correlation KW - turbo principle KW - two-dimensional hidden Markov model KW - Correlation KW - Decoding KW - Estimation KW - Iterative decoding KW - Noise KW - Speech KW - Vectors SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation ER - TY - CONF AB - In this paper we present a novel noise power spectral density tracking algorithm and its use in single-channel speech enhancement. It has the unique feature that it is able to track the noise statistics even if speech is dominant in a given time-frequency bin. As a consequence it can follow non-stationary noise superposed by speech, even in the critical case of rising noise power. The algorithm requires an initial estimate of the power spectrum of speech and is thus meant to be used as a postprocessor to a first speech enhancement stage. An experimental comparison with a state-of-the-art noise tracking algorithm demonstrates lower estimation errors under low SNR conditions and smaller fluctuations of the estimated values, resulting in improved speech quality as measured by PESQ scores. AU - Chinaev, Aleksej AU - Krueger, Alexander AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11745 KW - MAP parameter estimation KW - noise power estimation KW - speech enhancement T2 - 37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012) TI - Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor ER - TY - CONF AB - 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. AU - Krueger, Alexander AU - Haeb-Umbach, Reinhold ID - 11845 KW - Gaussian processes KW - MAP-based estimation KW - maximum a posteriori method KW - maximum likelihood estimation KW - nonstationary Gaussian processes T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) TI - MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations ER - TY - JOUR AB - 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. AU - Krueger, Alexander AU - Warsitz, Ernst AU - Haeb-Umbach, Reinhold ID - 11850 IS - 1 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - acoustical transfer function ratio KW - adaptive eigenvector tracking KW - array signal processing KW - beamformer design KW - blocking matrix KW - eigenvalues and eigenfunctions KW - eigenvector-based transfer function ratios estimation KW - generalized sidelobe canceler KW - interference reduction KW - iterative methods KW - power iteration method KW - reduced speech distortions KW - reverberant enclosure KW - reverberation KW - speech enhancement KW - stationary noise TI - Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation VL - 19 ER - TY - CONF AU - Kenter, Tobias AU - Platzner, Marco AU - Plessl, Christian AU - Kauschke, Michael ID - 2200 KW - design space exploration KW - LLVM KW - partitioning KW - performance KW - estimation KW - funding-intel SN - 978-1-4503-0554-9 T2 - Proc. Int. Symp. on Field-Programmable Gate Arrays (FPGA) TI - Performance Estimation Framework for Automated Exploration of CPU-Accelerator Architectures ER - TY - CONF AB - In this paper we present a robust location estimation algorithm especially focused on the accuracy in vertical position. A loosely-coupled error state space Kalman filter, which fuses sensor data of an Inertial Measurement Unit and the output of a Global Positioning System device, is augmented by height information from an altitude measurement unit. This unit consists of a barometric altimeter whose output is fused with topographic map information by a Kalman filter to provide robust information about the current vertical user position. These data replace the less reliable vertical position information provided the GPS device. It is shown that typical barometric errors like thermal divergences and fluctuations in the pressure due to changing weather conditions can be compensated by the topographic map information and the barometric error Kalman filter. The resulting height information is shown not only to be more reliable than height information provided by GPS. It also turns out that it leads to better attitude and thus better overall localization estimation accuracy due to the coupling of spatial orientations via the Direct Cosine Matrix. Results are presented both for artificially generated and field test data, where the user is moving by car. AU - Bevermeier, Maik AU - Walter, Oliver AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11726 KW - altitude measurement unit KW - barometers KW - barometric altimeter KW - barometric error Kalman filter KW - barometric height estimation KW - direct cosine matrix KW - global positioning system KW - Global Positioning System KW - GPS device KW - height information KW - height measurement KW - inertial measurement unit KW - Kalman filters KW - loosely-coupled error state space Kalman filter KW - loosely-coupled Kalman-filter KW - map matching KW - robust information KW - robust location estimation KW - sensor fusion KW - topographic map information KW - vertical user position T2 - 7th Workshop on Positioning Navigation and Communication (WPNC 2010) TI - Barometric height estimation combined with map-matching in a loosely-coupled Kalman-filter ER - TY - JOUR AB - In this paper, we present a new technique for automatic speech recognition (ASR) in reverberant environments. Our approach is aimed at the enhancement of the logarithmic Mel power spectrum, which is computed at an intermediate stage to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean square error estimate of the clean LMPSCs is computed by carrying out Bayesian inference. We employ switching linear dynamical models as an a priori model for the dynamics of the clean LMPSCs. Further, we derive a stochastic observation model which relates the clean to the reverberant LMPSCs through a simplified model of the room impulse response (RIR). This model requires 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 studied on the AURORA5 database and compared to that of constrained maximum-likelihood linear regression (CMLLR). It is shown by experimental results that our approach significantly outperforms CMLLR and that up to 80\% of the errors caused by the reverberation are recovered. In addition to the fact that the approach is compatible with the standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of moderate computational complexity and suitable for real time applications. AU - Krueger, Alexander AU - Haeb-Umbach, Reinhold ID - 11846 IS - 7 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - ASR KW - AURORA5 database KW - automatic speech recognition KW - Bayesian inference KW - belief networks KW - CMLLR KW - computational complexity KW - constrained maximum likelihood linear regression KW - least mean squares methods KW - LMPSC computation KW - logarithmic Mel power spectrum KW - maximum likelihood estimation KW - Mel frequency cepstral coefficients KW - MFCC feature vectors KW - microphone signal KW - minimum mean square error estimation KW - model-based feature enhancement KW - regression analysis KW - reverberant speech recognition KW - reverberation KW - RIR energy KW - room impulse response KW - speech recognition KW - stochastic observation model KW - stochastic processes TI - Model-Based Feature Enhancement for Reverberant Speech Recognition VL - 18 ER - TY - CONF AB - In this paper we present a novel vehicle tracking algorithm, which is based on multi-level sensor fusion of GPS (global positioning system) with Inertial Measurement Unit sensor data. It is shown that the robustness of the system to temporary dropouts of the GPS signal, which may occur due to limited visibility of satellites in narrow street canyons or tunnels, is greatly improved by sensor fusion. We further demonstrate how the observation and state noise covariances of the employed Kalman filters can be estimated alongside the filtering by an application of the Expectation-Maximization algorithm. The proposed time-variant multi-level Kalman filter is shown to outperform an Interacting Multiple Model approach while at the same time being computationally less demanding. AU - Bevermeier, Maik AU - Peschke, Sven AU - Haeb-Umbach, Reinhold ID - 11723 KW - covariance matrices KW - expectation-maximisation algorithm KW - expectation-maximization algorithm KW - global positioning system KW - Global Positioning System KW - GPS KW - inertial measurement unit KW - interacting multiple model approach KW - Kalman filters KW - multilevel sensor fusion KW - narrow street canyons KW - narrow tunnels KW - online parameter estimation KW - parameter estimation KW - road vehicles KW - robust vehicle localization KW - sensor fusion KW - state noise covariances KW - time-variant multilevel Kalman filter KW - vehicle tracking algorithm T2 - 6th Workshop on Positioning Navigation and Communication (WPNC 2009) TI - Robust vehicle localization based on multi-level sensor fusion and online parameter estimation ER -