---
_id: '11716'
abstract:
- lang: eng
text: 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:
- first_name: Ahmed H.
full_name: Abdelaziz, Ahmed H.
last_name: Abdelaziz
- first_name: Steffen
full_name: Zeiler, Steffen
last_name: Zeiler
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984'
apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach,
R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal
Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).
https://doi.org/10.1109/ICASSP.2013.6638984
bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
significance decoding}, DOI={10.1109/ICASSP.2013.6638984},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
}'
chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984.
ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP),
2013 IEEE International Conference on, 2013, pp. 6827–6831.
mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984.
short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- 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
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on
publication_identifier:
issn:
- 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11816'
abstract:
- lang: eng
text: 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:
- first_name: Manh Kha
full_name: Hoang, Manh Kha
last_name: Hoang
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
Gaussian data with application to WiFi indoor positioning. In: 38th International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013). ; 2013:3721-3725.
doi:10.1109/ICASSP.2013.6638353'
apa: Hoang, M. K., & Haeb-Umbach, R. (2013). Parameter estimation and classification
of censored Gaussian data with application to WiFi indoor positioning. In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)
(pp. 3721–3725). https://doi.org/10.1109/ICASSP.2013.6638353
bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
classification of censored Gaussian data with application to WiFi indoor positioning},
DOI={10.1109/ICASSP.2013.6638353},
booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
pages={3721–3725} }'
chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
3721–25, 2013. https://doi.org/10.1109/ICASSP.2013.6638353.
ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
censored Gaussian data with application to WiFi indoor positioning,” in 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–3725.
mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
of Censored Gaussian Data with Application to WiFi Indoor Positioning.” 38th
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013),
2013, pp. 3721–25, doi:10.1109/ICASSP.2013.6638353.
short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11845'
abstract:
- lang: eng
text: 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:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Krueger A, Haeb-Umbach R. MAP-based estimation of the parameters of non-stationary
Gaussian processes from noisy observations. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2011). ; 2011:3596-3599.
doi:10.1109/ICASSP.2011.5946256'
apa: Krueger, A., & Haeb-Umbach, R. (2011). MAP-based estimation of the parameters
of non-stationary Gaussian processes from noisy observations. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2011) (pp. 3596–3599).
https://doi.org/10.1109/ICASSP.2011.5946256
bibtex: '@inproceedings{Krueger_Haeb-Umbach_2011, title={MAP-based estimation of
the parameters of non-stationary Gaussian processes from noisy observations},
DOI={10.1109/ICASSP.2011.5946256},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011)}, author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2011},
pages={3596–3599} }'
chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of
the Parameters of Non-Stationary Gaussian Processes from Noisy Observations.”
In IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011), 3596–99, 2011. https://doi.org/10.1109/ICASSP.2011.5946256.
ieee: A. Krueger and R. Haeb-Umbach, “MAP-based estimation of the parameters of
non-stationary Gaussian processes from noisy observations,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2011), 2011,
pp. 3596–3599.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of Non-Stationary Gaussian Processes from Noisy Observations.” IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2011),
2011, pp. 3596–99, doi:10.1109/ICASSP.2011.5946256.
short: 'A. Krueger, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2011), 2011, pp. 3596–3599.'
date_created: 2019-07-12T05:29:22Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2011.5946256
keyword:
- Gaussian processes
- MAP-based estimation
- maximum a posteriori method
- maximum likelihood estimation
- nonstationary Gaussian processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/KrHa11.pdf
oa: '1'
page: 3596-3599
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2011)
status: public
title: MAP-based estimation of the parameters of non-stationary Gaussian processes
from noisy observations
type: conference
user_id: '44006'
year: '2011'
...
---
_id: '11785'
abstract:
- lang: eng
text: '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:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
citation:
ama: 'Haeb-Umbach R, Bevermeier M. OFDM Channel Estimation Based on Combined Estimation
in Time and Frequency Domain. In: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2007). Vol 3. ; 2007:III-277-III-280.
doi:10.1109/ICASSP.2007.366526'
apa: Haeb-Umbach, R., & Bevermeier, M. (2007). OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2007) (Vol.
3, pp. III-277-III–280). https://doi.org/10.1109/ICASSP.2007.366526
bibtex: '@inproceedings{Haeb-Umbach_Bevermeier_2007, title={OFDM Channel Estimation
Based on Combined Estimation in Time and Frequency Domain}, volume={3}, DOI={10.1109/ICASSP.2007.366526},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2007)}, author={Haeb-Umbach, Reinhold and Bevermeier, Maik}, year={2007},
pages={III-277-III–280} }'
chicago: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain.” In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2007), 3:III-277-III–280,
2007. https://doi.org/10.1109/ICASSP.2007.366526.
ieee: R. Haeb-Umbach and M. Bevermeier, “OFDM Channel Estimation Based on Combined
Estimation in Time and Frequency Domain,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2007), 2007, vol. 3, pp.
III-277-III–280.
mla: Haeb-Umbach, Reinhold, and Maik Bevermeier. “OFDM Channel Estimation Based
on Combined Estimation in Time and Frequency Domain.” IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2007), vol. 3, 2007, pp.
III-277-III–280, doi:10.1109/ICASSP.2007.366526.
short: 'R. Haeb-Umbach, M. Bevermeier, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2007), 2007, pp. III-277-III–280.'
date_created: 2019-07-12T05:28:13Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2007.366526
intvolume: ' 3'
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/HaBe07.pdf
oa: '1'
page: III-277-III-280
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2007)
status: public
title: OFDM Channel Estimation Based on Combined Estimation in Time and Frequency
Domain
type: conference
user_id: '44006'
volume: 3
year: '2007'
...