@inproceedings{11716,
  abstract     = {{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.}},
  author       = {{Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea and Leutnant, Volker and Haeb-Umbach, Reinhold}},
  booktitle    = {{Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on}},
  issn         = {{1520-6149}},
  keywords     = {{Bayes methods, Gaussian processes, convolution, decision theory, decoding, noise, reverberation, speech coding, speech recognition, Bayesian decision rule, GMM, Gaussian mixture models, additive noise scenarios, automatic speech recognition systems, convolutive noise scenarios, decoding approach, mathematical framework, reverberant environments, significance decoding, speech feature estimation, uncertainty-of-observation techniques, Hidden Markov models, Maximum likelihood decoding, Noise, Speech, Speech recognition, Uncertainty, Uncertainty-of-observation, modified imputation, noise robust speech recognition, significance decoding, uncertainty decoding}},
  pages        = {{6827--6831}},
  title        = {{{GMM-based significance decoding}}},
  doi          = {{10.1109/ICASSP.2013.6638984}},
  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{11845,
  abstract     = {{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.}},
  author       = {{Krueger, Alexander and Haeb-Umbach, Reinhold}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011)}},
  keywords     = {{Gaussian processes, MAP-based estimation, maximum a posteriori method, maximum likelihood estimation, nonstationary Gaussian processes}},
  pages        = {{3596--3599}},
  title        = {{{MAP-based estimation of the parameters of non-stationary Gaussian processes from noisy observations}}},
  doi          = {{10.1109/ICASSP.2011.5946256}},
  year         = {{2011}},
}

@inproceedings{11785,
  abstract     = {{In this paper we present a novel channel impulse response estimation technique for block-oriented OFDM transmission based on combining estimators: the estimates provided by a Kalman filter operating in the time domain and a Wiener filter in the frequency domain are optimally combined by taking into account their estimated error covariances. The resulting estimator turns out to be identical to the MAP estimator of correlated jointly Gaussian mean vectors. Different variants of the proposed scheme are experimentally investigated in an EEEE 802.11a-like system setup. They compare favourably with known approaches from the literature resulting in reduced mean square estimation error and bit error rate. Further, robustness and complexity issues are discussed}},
  author       = {{Haeb-Umbach, Reinhold and Bevermeier, Maik}},
  booktitle    = {{IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2007)}},
  keywords     = {{bit error rate, block-oriented OFDM transmission, channel estimation, channel impulse response estimation, combining estimators, error statistics, frequency domain estimation, Gaussian mean vectors, Gaussian processes, Kalman filter, Kalman filters, MAP estimator, maximum likelihood estimation, OFDM channel estimation, OFDM modulation, time domain estimation, time-frequency analysis, Wiener filter, Wiener filters}},
  pages        = {{III--277--III--280}},
  title        = {{{OFDM Channel Estimation Based on Combined Estimation in Time and Frequency Domain}}},
  doi          = {{10.1109/ICASSP.2007.366526}},
  volume       = {{3}},
  year         = {{2007}},
}