@article{63800,
  abstract     = {{In this contribution, we address the estimation of the frequency-dependent elastic parameters of polymers in the ultrasound range, which is formulated as an inverse problem. This inverse problem is implemented as a nonlinear regression-type optimization problem, in which the simulation signals are fitted to the measurement signals. These signals consist of displacement responses in waveguides, focusing on hollow cylindrical geometries to enhance the simulation efficiency. To accelerate the optimization and reduce the number of model evaluations and wait times, we propose two novel methods. First, we introduce an adaptation of the Levenberg–Marquardt method derived from a geometrical interpretation of the least-squares optimization problem. Second, we introduce an improved objective function based on the autocorrelated envelopes of the measurement and simulation signals. Given that this study primarily relies on simulation data to quantify optimization convergence, we aggregate the expected ranges of realistic material parameters and derive their distributions to ensure the reproducibility of optimizations with proper measurements. We demonstrate the effectiveness of our objective function modification and step adaptation for various materials with isotropic material symmetry by comparing them with the Broyden–Fletcher–Goldfarb–Shanno method. In all cases, our method reduces the total number of model evaluations, thereby shortening the time to identify the material parameters.}},
  author       = {{Itner, Dominik and Dreiling, Dmitrij and Gravenkamp, Hauke and Henning, Bernd and Birk, Carolin}},
  issn         = {{0888-3270}},
  journal      = {{Mechanical Systems and Signal Processing}},
  keywords     = {{Material parameter estimation, Waveguide, Nonlinear optimization, Inverse problem, Least squares}},
  pages        = {{113904}},
  title        = {{{A modified Levenberg–Marquardt method for estimating the elastic material parameters of polymer waveguides using residuals between autocorrelated frequency responses}}},
  doi          = {{https://doi.org/10.1016/j.ymssp.2026.113904}},
  volume       = {{247}},
  year         = {{2026}},
}

@article{59755,
  abstract     = {{Due to the application of Artificial Intelligence (AI) in high-risk domains like law or medicine,
trustworthy AI and trust in AI are of increasing scientific and public relevance. A typical conception,
for example in the context of medical diagnosis, is that a knowledgeable user receives AIgenerated
classification as advice. Research to improve such interactions often aims to foster the
user’s trust, which in turn should improve the combined human-AI performance. Given that AI
models can err, we argue that the possibility to critically review, thus to distrust, an AI decision is
an equally interesting target of research.
We created two image classification scenarios in which the participants received mock-up
AI advice. The quality of the advice decreases for a phase of the experiment. We studied the
task performance, trust and distrust of the participants, and tested whether an instruction to
remain skeptical and review each piece of advice led to a better performance compared to a
neutral condition. Our results indicate that this instruction does not improve but rather worsens
the participants’ performance. Repeated single-item self-report of trust and distrust shows an
increase in trust and a decrease in distrust after the drop in the AI’s classification quality, with no
difference between the two instructions. Furthermore, via a Bayesian Signal Detection Theory
analysis, we provide a procedure to assess appropriate reliance in detail, by quantifying whether
the problems of under- and over-reliance have been mitigated. We discuss implications of our
results for the usage of disclaimers before interacting with AI, as prominently used in current
LLM-based chatbots, and for trust and distrust research.}},
  author       = {{Peters, Tobias Martin and Scharlau, Ingrid}},
  journal      = {{Frontiers in Psychology}},
  keywords     = {{trust in AI, trust, distrust, human-AI interaction, Signal Detection Theory, Bayesian parameter estimation, image classification}},
  title        = {{{Interacting with fallible AI: Is distrust helpful when receiving AI misclassifications?}}},
  doi          = {{10.3389/fpsyg.2025.1574809}},
  volume       = {{16}},
  year         = {{2025}},
}

@inproceedings{11740,
  abstract     = {{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.}},
  author       = {{Chinaev, Aleksej and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian noise, maximum likelihood estimation, parameter estimation, GMM parameter, Gaussian mixture model, MAP estimation, Map-based estimation, maximum a-posteriori estimation, maximum likelihood technique, noisy observation, sequential estimation framework, white Gaussian noise, Additive noise, Gaussian mixture model, Maximum likelihood estimation, Noise measurement, Gaussian mixture model, Maximum a posteriori estimation, Maximum likelihood estimation}},
  pages        = {{3352--3356}},
  title        = {{{MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations}}},
  doi          = {{10.1109/ICASSP.2013.6638279}},
  year         = {{2013}},
}

@inproceedings{11816,
  abstract     = {{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.}},
  author       = {{Hoang, Manh Kha and Haeb-Umbach, Reinhold}},
  booktitle    = {{38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)}},
  issn         = {{1520-6149}},
  keywords     = {{Gaussian processes, Global Positioning System, convergence, expectation-maximisation algorithm, fingerprint identification, indoor radio, signal classification, wireless LAN, EM algorithm, ML estimation, WiFi indoor positioning, censored Gaussian data classification, clipped data, convergence properties, expectation maximization algorithm, fingerprinting method, maximum likelihood estimation, optimal classification, parameters estimation, portable devices sensitivity, signal strength measurements, wireless LAN positioning systems, Convergence, IEEE 802.11 Standards, Maximum likelihood estimation, Parameter estimation, Position measurement, Training, Indoor positioning, censored data, expectation maximization, signal strength, wireless LAN}},
  pages        = {{3721--3725}},
  title        = {{{Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning}}},
  doi          = {{10.1109/ICASSP.2013.6638353}},
  year         = {{2013}},
}

@inproceedings{11745,
  abstract     = {{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.}},
  author       = {{Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}},
  booktitle    = {{37th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)}},
  keywords     = {{MAP parameter estimation, noise power estimation, speech enhancement}},
  title        = {{{Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor}}},
  year         = {{2012}},
}

@inproceedings{11723,
  abstract     = {{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.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{6th Workshop on Positioning Navigation and Communication (WPNC 2009)}},
  keywords     = {{covariance matrices, expectation-maximisation algorithm, expectation-maximization algorithm, global positioning system, Global Positioning System, GPS, inertial measurement unit, interacting multiple model approach, Kalman filters, multilevel sensor fusion, narrow street canyons, narrow tunnels, online parameter estimation, parameter estimation, road vehicles, robust vehicle localization, sensor fusion, state noise covariances, time-variant multilevel Kalman filter, vehicle tracking algorithm}},
  pages        = {{235--242}},
  title        = {{{Robust vehicle localization based on multi-level sensor fusion and online parameter estimation}}},
  doi          = {{10.1109/WPNC.2009.4907833}},
  year         = {{2009}},
}

@inproceedings{11724,
  abstract     = {{In this paper we present a novel vehicle tracking method which is based on multi-stage Kalman filtering of GPS and IMU sensor data. After individual Kalman filtering of GPS and IMU measurements the estimates of the orientation of the vehicle are combined in an optimal manner to improve the robustness towards drift errors. The tracking algorithm incorporates the estimation of time-variant covariance parameters by using an iterative block Expectation-Maximization algorithm to account for time-variant driving conditions and measurement quality. The proposed system is compared to an interacting multiple model approach (IMM) and achieves improved localization accuracy at lower computational complexity. Furthermore we show how the joint parameter estimation and localizaiton can be conducted with streaming input data to be able to track vehicles in a real driving environment.}},
  author       = {{Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}},
  keywords     = {{computational complexity, expectation-maximisation algorithm, Global Positioning System, inertial measurement unit, inertial navigation, interacting multiple model, iterative block expectation-maximization algorithm, Kalman filters, multi-stage Kalman filter, parameter estimation, road vehicles, vehicle positioning, vehicle tracking}},
  pages        = {{1--5}},
  title        = {{{Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning}}},
  doi          = {{10.1109/VETECS.2009.5073634}},
  year         = {{2009}},
}

@article{11938,
  abstract     = {{In this paper, parameter estimation of a state-space model of noise or noisy speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation of the state and observation noise covariance from noise-only input data. It is supposed to be used during the offline training mode of a speech recognizer. Further a sequential online EM algorithm is developed to adapt the observation noise covariance on noisy speech cepstra at its input. The estimated parameters are then used in model-based speech feature enhancement for noise-robust automatic speech recognition. Experiments on the AURORA4 database lead to improved recognition results with a linear state model compared to the assumption of stationary noise.}},
  author       = {{Windmann, Stefan and Haeb-Umbach, Reinhold}},
  journal      = {{IEEE Transactions on Audio, Speech, and Language Processing}},
  keywords     = {{AURORA4 database, blockwise EM algorithm, covariance analysis, linear state model, noise covariance, noise-robust automatic speech recognition, noisy speech cepstra, offline training mode, parameter estimation, speech recognition, speech recognition equipment, speech recognizer, state-space methods, state-space model}},
  number       = {{8}},
  pages        = {{1577--1590}},
  title        = {{{Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition}}},
  doi          = {{10.1109/TASL.2009.2023172}},
  volume       = {{17}},
  year         = {{2009}},
}