---
_id: '11925'
abstract:
- lang: eng
text: In this paper we present a system for car navigation by fusing sensor data
on an Android smartphone. The key idea is to use both the internal sensors of
the smartphone (e.g., gyroscope) and sensor data from the car (e.g., speed information)
to support navigation via GPS. To this end we employ a CAN-Bus-to-Bluetooth adapter
to establish a wireless connection between the smartphone and the CAN-Bus of the
car. On the smartphone a strapdown algorithm and an error-state Kalman filter
are used to fuse the different sensor data streams. The experimental results show
that the system is able to maintain higher positioning accuracy during GPS dropouts,
thus improving the availability and reliability, compared to GPS-only solutions.
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Andreas
full_name: Engler, Andreas
last_name: Engler
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Walter O, Schmalenstroeer J, Engler A, Haeb-Umbach R. Smartphone-Based Sensor
Fusion for Improved Vehicular Navigation. In: 9th Workshop on Positioning Navigation
and Communication (WPNC 2012). ; 2012.'
apa: Walter, O., Schmalenstroeer, J., Engler, A., & Haeb-Umbach, R. (2012).
Smartphone-Based Sensor Fusion for Improved Vehicular Navigation. 9th Workshop
on Positioning Navigation and Communication (WPNC 2012).
bibtex: '@inproceedings{Walter_Schmalenstroeer_Engler_Haeb-Umbach_2012, title={Smartphone-Based
Sensor Fusion for Improved Vehicular Navigation}, booktitle={9th Workshop on Positioning
Navigation and Communication (WPNC 2012)}, author={Walter, Oliver and Schmalenstroeer,
Joerg and Engler, Andreas and Haeb-Umbach, Reinhold}, year={2012} }'
chicago: Walter, Oliver, Joerg Schmalenstroeer, Andreas Engler, and Reinhold Haeb-Umbach.
“Smartphone-Based Sensor Fusion for Improved Vehicular Navigation.” In 9th
Workshop on Positioning Navigation and Communication (WPNC 2012), 2012.
ieee: O. Walter, J. Schmalenstroeer, A. Engler, and R. Haeb-Umbach, “Smartphone-Based
Sensor Fusion for Improved Vehicular Navigation,” 2012.
mla: Walter, Oliver, et al. “Smartphone-Based Sensor Fusion for Improved Vehicular
Navigation.” 9th Workshop on Positioning Navigation and Communication (WPNC
2012), 2012.
short: 'O. Walter, J. Schmalenstroeer, A. Engler, R. Haeb-Umbach, in: 9th Workshop
on Positioning Navigation and Communication (WPNC 2012), 2012.'
date_created: 2019-07-12T05:30:54Z
date_updated: 2023-10-26T08:13:27Z
department:
- _id: '54'
keyword:
- Smartphone
- navigation
- sensor fusion
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2012/WaScEnHa12.pdf
oa: '1'
publication: 9th Workshop on Positioning Navigation and Communication (WPNC 2012)
quality_controlled: '1'
status: public
title: Smartphone-Based Sensor Fusion for Improved Vehicular Navigation
type: conference
user_id: '460'
year: '2012'
...
---
_id: '11721'
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Stephan
full_name: Flanke, Stephan
last_name: Flanke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Jan
full_name: Stehr, Jan
last_name: Stehr
citation:
ama: 'Bevermeier M, Flanke S, Haeb-Umbach R, Stehr J. A Platform for efficient Supply
Chain Management Support in Logistics. In: International Workshop on Intelligent
Transportation (WIT 2011). ; 2011.'
apa: Bevermeier, M., Flanke, S., Haeb-Umbach, R., & Stehr, J. (2011). A Platform
for efficient Supply Chain Management Support in Logistics. In International
Workshop on Intelligent Transportation (WIT 2011).
bibtex: '@inproceedings{Bevermeier_Flanke_Haeb-Umbach_Stehr_2011, title={A Platform
for efficient Supply Chain Management Support in Logistics}, booktitle={International
Workshop on Intelligent Transportation (WIT 2011)}, author={Bevermeier, Maik and
Flanke, Stephan and Haeb-Umbach, Reinhold and Stehr, Jan}, year={2011} }'
chicago: Bevermeier, Maik, Stephan Flanke, Reinhold Haeb-Umbach, and Jan Stehr.
“A Platform for Efficient Supply Chain Management Support in Logistics.” In International
Workshop on Intelligent Transportation (WIT 2011), 2011.
ieee: M. Bevermeier, S. Flanke, R. Haeb-Umbach, and J. Stehr, “A Platform for efficient
Supply Chain Management Support in Logistics,” in International Workshop on
Intelligent Transportation (WIT 2011), 2011.
mla: Bevermeier, Maik, et al. “A Platform for Efficient Supply Chain Management
Support in Logistics.” International Workshop on Intelligent Transportation
(WIT 2011), 2011.
short: 'M. Bevermeier, S. Flanke, R. Haeb-Umbach, J. Stehr, in: International Workshop
on Intelligent Transportation (WIT 2011), 2011.'
date_created: 2019-07-12T05:26:58Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/BeFlHaSt11.pdf
oa: '1'
publication: International Workshop on Intelligent Transportation (WIT 2011)
status: public
title: A Platform for efficient Supply Chain Management Support in Logistics
type: conference
user_id: '44006'
year: '2011'
...
---
_id: '11774'
abstract:
- lang: eng
text: 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.
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Haeb-Umbach R. Uncertainty Decoding and Conditional Bayesian Estimation. In:
Haeb-Umbach R, Kolossa D, eds. Robust Speech Recognition of Uncertain or Missing
Data. Springer; 2011.'
apa: Haeb-Umbach, R. (2011). Uncertainty Decoding and Conditional Bayesian Estimation.
In R. Haeb-Umbach & D. Kolossa (Eds.), Robust Speech Recognition of Uncertain
or Missing Data. Springer.
bibtex: '@inbook{Haeb-Umbach_2011, title={Uncertainty Decoding and Conditional Bayesian
Estimation}, booktitle={Robust Speech Recognition of Uncertain or Missing Data},
publisher={Springer}, author={Haeb-Umbach, Reinhold}, editor={Haeb-Umbach, Reinhold
and Kolossa, DorotheaEditors}, year={2011} }'
chicago: Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.”
In Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa. Springer, 2011.
ieee: R. Haeb-Umbach, “Uncertainty Decoding and Conditional Bayesian Estimation,”
in Robust Speech Recognition of Uncertain or Missing Data, R. Haeb-Umbach
and D. Kolossa, Eds. Springer, 2011.
mla: Haeb-Umbach, Reinhold. “Uncertainty Decoding and Conditional Bayesian Estimation.”
Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa, Springer, 2011.
short: 'R. Haeb-Umbach, in: R. Haeb-Umbach, D. Kolossa (Eds.), Robust Speech Recognition
of Uncertain or Missing Data, Springer, 2011.'
date_created: 2019-07-12T05:28:00Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
editor:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
last_name: Haeb-Umbach
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
language:
- iso: eng
publication: Robust Speech Recognition of Uncertain or Missing Data
publisher: Springer
status: public
title: Uncertainty Decoding and Conditional Bayesian Estimation
type: book_chapter
user_id: '44006'
year: '2011'
...
---
_id: '11775'
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Haeb-Umbach R. Können Computer sprechen und hören, sollen sie es überhaupt
können? Sprachverarbeitung und ambiente Intelligenz. In: Baustelle Informationsgesellschaft
Und Universität Heute. Ferdinand Schoeningh Verlag, Paderborn; 2011.'
apa: Haeb-Umbach, R. (2011). Können Computer sprechen und hören, sollen sie es überhaupt
können? Sprachverarbeitung und ambiente Intelligenz. In Baustelle Informationsgesellschaft
und Universität heute. Ferdinand Schoeningh Verlag, Paderborn.
bibtex: '@inbook{Haeb-Umbach_2011, title={Können Computer sprechen und hören, sollen
sie es überhaupt können? Sprachverarbeitung und ambiente Intelligenz}, booktitle={Baustelle
Informationsgesellschaft und Universität heute}, publisher={Ferdinand Schoeningh
Verlag, Paderborn}, author={Haeb-Umbach, Reinhold}, year={2011} }'
chicago: Haeb-Umbach, Reinhold. “Können Computer Sprechen Und Hören, Sollen Sie
Es Überhaupt Können? Sprachverarbeitung Und Ambiente Intelligenz.” In Baustelle
Informationsgesellschaft Und Universität Heute. Ferdinand Schoeningh Verlag,
Paderborn, 2011.
ieee: R. Haeb-Umbach, “Können Computer sprechen und hören, sollen sie es überhaupt
können? Sprachverarbeitung und ambiente Intelligenz,” in Baustelle Informationsgesellschaft
und Universität heute, Ferdinand Schoeningh Verlag, Paderborn, 2011.
mla: Haeb-Umbach, Reinhold. “Können Computer Sprechen Und Hören, Sollen Sie Es Überhaupt
Können? Sprachverarbeitung Und Ambiente Intelligenz.” Baustelle Informationsgesellschaft
Und Universität Heute, Ferdinand Schoeningh Verlag, Paderborn, 2011.
short: 'R. Haeb-Umbach, in: Baustelle Informationsgesellschaft Und Universität Heute,
Ferdinand Schoeningh Verlag, Paderborn, 2011.'
date_created: 2019-07-12T05:28:01Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
publication: Baustelle Informationsgesellschaft und Universität heute
publisher: Ferdinand Schoeningh Verlag, Paderborn
status: public
title: Können Computer sprechen und hören, sollen sie es überhaupt können? Sprachverarbeitung
und ambiente Intelligenz
type: book_chapter
user_id: '44006'
year: '2011'
...
---
_id: '11807'
author:
- first_name: Tobias
full_name: Herbig, Tobias
last_name: Herbig
- first_name: Franz
full_name: Gerl, Franz
last_name: Gerl
- first_name: Wolfgang
full_name: Minker, Wolfgang
last_name: Minker
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Herbig T, Gerl F, Minker W, Haeb-Umbach R. Adaptive Systems for Unsupervised
Speaker Tracking and Speech Recognition. Evolving Systems. 2011;2(3):199-214.
apa: Herbig, T., Gerl, F., Minker, W., & Haeb-Umbach, R. (2011). Adaptive Systems
for Unsupervised Speaker Tracking and Speech Recognition. Evolving Systems,
2(3), 199–214.
bibtex: '@article{Herbig_Gerl_Minker_Haeb-Umbach_2011, title={Adaptive Systems for
Unsupervised Speaker Tracking and Speech Recognition}, volume={2}, number={3},
journal={Evolving Systems}, author={Herbig, Tobias and Gerl, Franz and Minker,
Wolfgang and Haeb-Umbach, Reinhold}, year={2011}, pages={199–214} }'
chicago: 'Herbig, Tobias, Franz Gerl, Wolfgang Minker, and Reinhold Haeb-Umbach.
“Adaptive Systems for Unsupervised Speaker Tracking and Speech Recognition.” Evolving
Systems 2, no. 3 (2011): 199–214.'
ieee: T. Herbig, F. Gerl, W. Minker, and R. Haeb-Umbach, “Adaptive Systems for Unsupervised
Speaker Tracking and Speech Recognition,” Evolving Systems, vol. 2, no.
3, pp. 199–214, 2011.
mla: Herbig, Tobias, et al. “Adaptive Systems for Unsupervised Speaker Tracking
and Speech Recognition.” Evolving Systems, vol. 2, no. 3, 2011, pp. 199–214.
short: T. Herbig, F. Gerl, W. Minker, R. Haeb-Umbach, Evolving Systems 2 (2011)
199–214.
date_created: 2019-07-12T05:28:38Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
intvolume: ' 2'
issue: '3'
language:
- iso: eng
page: 199-214
publication: Evolving Systems
status: public
title: Adaptive Systems for Unsupervised Speaker Tracking and Speech Recognition
type: journal_article
user_id: '44006'
volume: 2
year: '2011'
...
---
_id: '11843'
abstract:
- lang: eng
text: 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.
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. A Model-Based Approach to Joint Compensation of
Noise and Reverberation for Speech Recognition. In: Haeb-Umbach R, Kolossa D,
eds. Robust Speech Recognition of Uncertain or Missing Data. Springer;
2011.'
apa: Krueger, A., & Haeb-Umbach, R. (2011). A Model-Based Approach to Joint
Compensation of Noise and Reverberation for Speech Recognition. In R. Haeb-Umbach
& D. Kolossa (Eds.), Robust Speech Recognition of Uncertain or Missing
Data. Springer.
bibtex: '@inbook{Krueger_Haeb-Umbach_2011, title={A Model-Based Approach to Joint
Compensation of Noise and Reverberation for Speech Recognition}, booktitle={Robust
Speech Recognition of Uncertain or Missing Data}, publisher={Springer}, author={Krueger,
Alexander and Haeb-Umbach, Reinhold}, editor={Haeb-Umbach, Reinhold and Kolossa,
DorotheaEditors}, year={2011} }'
chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “A Model-Based Approach to
Joint Compensation of Noise and Reverberation for Speech Recognition.” In Robust
Speech Recognition of Uncertain or Missing Data, edited by Reinhold Haeb-Umbach
and Dorothea Kolossa. Springer, 2011.
ieee: A. Krueger and R. Haeb-Umbach, “A Model-Based Approach to Joint Compensation
of Noise and Reverberation for Speech Recognition,” in Robust Speech Recognition
of Uncertain or Missing Data, R. Haeb-Umbach and D. Kolossa, Eds. Springer,
2011.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “A Model-Based Approach to Joint
Compensation of Noise and Reverberation for Speech Recognition.” Robust Speech
Recognition of Uncertain or Missing Data, edited by Reinhold Haeb-Umbach and
Dorothea Kolossa, Springer, 2011.
short: 'A. Krueger, R. Haeb-Umbach, in: R. Haeb-Umbach, D. Kolossa (Eds.), Robust
Speech Recognition of Uncertain or Missing Data, Springer, 2011.'
date_created: 2019-07-12T05:29:20Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
editor:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
last_name: Haeb-Umbach
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
language:
- iso: eng
publication: Robust Speech Recognition of Uncertain or Missing Data
publisher: Springer
status: public
title: A Model-Based Approach to Joint Compensation of Noise and Reverberation for
Speech Recognition
type: book_chapter
user_id: '44006'
year: '2011'
...
---
_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: '11850'
abstract:
- lang: eng
text: 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.
author:
- first_name: Alexander
full_name: Krueger, Alexander
last_name: Krueger
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Krueger A, Warsitz E, Haeb-Umbach R. Speech Enhancement With a GSC-Like Structure
Employing Eigenvector-Based Transfer Function Ratios Estimation. IEEE Transactions
on Audio, Speech, and Language Processing. 2011;19(1):206-219. doi:10.1109/TASL.2010.2047324
apa: Krueger, A., Warsitz, E., & Haeb-Umbach, R. (2011). Speech Enhancement
With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
Estimation. IEEE Transactions on Audio, Speech, and Language Processing,
19(1), 206–219. https://doi.org/10.1109/TASL.2010.2047324
bibtex: '@article{Krueger_Warsitz_Haeb-Umbach_2011, title={Speech Enhancement With
a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation},
volume={19}, DOI={10.1109/TASL.2010.2047324},
number={1}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Krueger, Alexander and Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2011},
pages={206–219} }'
chicago: 'Krueger, Alexander, Ernst Warsitz, and Reinhold Haeb-Umbach. “Speech Enhancement
With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
Estimation.” IEEE Transactions on Audio, Speech, and Language Processing
19, no. 1 (2011): 206–19. https://doi.org/10.1109/TASL.2010.2047324.'
ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like
Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” IEEE
Transactions on Audio, Speech, and Language Processing, vol. 19, no. 1, pp.
206–219, 2011.
mla: Krueger, Alexander, et al. “Speech Enhancement With a GSC-Like Structure Employing
Eigenvector-Based Transfer Function Ratios Estimation.” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 19, no. 1, 2011, pp. 206–19,
doi:10.1109/TASL.2010.2047324.
short: A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
and Language Processing 19 (2011) 206–219.
date_created: 2019-07-12T05:29:28Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2047324
intvolume: ' 19'
issue: '1'
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/KrWaHa11.pdf
oa: '1'
page: 206-219
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer
Function Ratios Estimation
type: journal_article
user_id: '44006'
volume: 19
year: '2011'
...
---
_id: '11856'
abstract:
- lang: eng
text: '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.'
author:
- 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: 'Leutnant V, Haeb-Umbach R. Conditional Bayesian Estimation Employing a Phase-Sensitive
Observation Model for Noise Robust Speech Recognition. In: Haeb-Umbach R, Kolossa
D, eds. Robust Speech Recognition of Uncertain or Missing Data. Springer;
2011.'
apa: Leutnant, V., & Haeb-Umbach, R. (2011). Conditional Bayesian Estimation
Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition.
In R. Haeb-Umbach & D. Kolossa (Eds.), Robust Speech Recognition of Uncertain
or Missing Data. Springer.
bibtex: '@inbook{Leutnant_Haeb-Umbach_2011, title={Conditional Bayesian Estimation
Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition},
booktitle={Robust Speech Recognition of Uncertain or Missing Data}, publisher={Springer},
author={Leutnant, Volker and Haeb-Umbach, Reinhold}, editor={Haeb-Umbach, Reinhold
and Kolossa, DorotheaEditors}, year={2011} }'
chicago: Leutnant, Volker, and Reinhold Haeb-Umbach. “Conditional Bayesian Estimation
Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition.”
In Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa. Springer, 2011.
ieee: V. Leutnant and R. Haeb-Umbach, “Conditional Bayesian Estimation Employing
a Phase-Sensitive Observation Model for Noise Robust Speech Recognition,” in Robust
Speech Recognition of Uncertain or Missing Data, R. Haeb-Umbach and D. Kolossa,
Eds. Springer, 2011.
mla: Leutnant, Volker, and Reinhold Haeb-Umbach. “Conditional Bayesian Estimation
Employing a Phase-Sensitive Observation Model for Noise Robust Speech Recognition.”
Robust Speech Recognition of Uncertain or Missing Data, edited by Reinhold
Haeb-Umbach and Dorothea Kolossa, Springer, 2011.
short: 'V. Leutnant, R. Haeb-Umbach, in: R. Haeb-Umbach, D. Kolossa (Eds.), Robust
Speech Recognition of Uncertain or Missing Data, Springer, 2011.'
date_created: 2019-07-12T05:29:35Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
editor:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
last_name: Haeb-Umbach
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
language:
- iso: eng
publication: Robust Speech Recognition of Uncertain or Missing Data
publisher: Springer
status: public
title: Conditional Bayesian Estimation Employing a Phase-Sensitive Observation Model
for Noise Robust Speech Recognition
type: book_chapter
user_id: '44006'
year: '2011'
...
---
_id: '11866'
abstract:
- lang: eng
text: 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.
author:
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- 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: 'Leutnant V, Krueger A, Haeb-Umbach R. A versatile Gaussian splitting approach
to non-linear state estimation and its application to noise-robust ASR. In: Interspeech
2011. ; 2011.'
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2011). A versatile Gaussian
splitting approach to non-linear state estimation and its application to noise-robust
ASR. In Interspeech 2011.
bibtex: '@inproceedings{Leutnant_Krueger_Haeb-Umbach_2011, title={A versatile Gaussian
splitting approach to non-linear state estimation and its application to noise-robust
ASR}, booktitle={Interspeech 2011}, author={Leutnant, Volker and Krueger, Alexander
and Haeb-Umbach, Reinhold}, year={2011} }'
chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A Versatile
Gaussian Splitting Approach to Non-Linear State Estimation and Its Application
to Noise-Robust ASR.” In Interspeech 2011, 2011.
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A versatile Gaussian splitting
approach to non-linear state estimation and its application to noise-robust ASR,”
in Interspeech 2011, 2011.
mla: Leutnant, Volker, et al. “A Versatile Gaussian Splitting Approach to Non-Linear
State Estimation and Its Application to Noise-Robust ASR.” Interspeech 2011,
2011.
short: 'V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Interspeech 2011, 2011.'
date_created: 2019-07-12T05:29:46Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/LeKrHa11.pdf
oa: '1'
publication: Interspeech 2011
status: public
title: A versatile Gaussian splitting approach to non-linear state estimation and
its application to noise-robust ASR
type: conference
user_id: '44006'
year: '2011'
...
---
_id: '11911'
abstract:
- lang: eng
text: 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.
author:
- first_name: Dang Hai
full_name: Tran Vu, Dang Hai
last_name: Tran Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Tran Vu DH, Haeb-Umbach R. On Initial Seed Selection for Frequency Domain
Blind Speech Separation. In: Interspeech 2011. ; 2011.'
apa: Tran Vu, D. H., & Haeb-Umbach, R. (2011). On Initial Seed Selection for
Frequency Domain Blind Speech Separation. In Interspeech 2011.
bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2011, title={On Initial Seed Selection
for Frequency Domain Blind Speech Separation}, booktitle={Interspeech 2011}, author={Tran
Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2011} }'
chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “On Initial Seed Selection
for Frequency Domain Blind Speech Separation.” In Interspeech 2011, 2011.
ieee: D. H. Tran Vu and R. Haeb-Umbach, “On Initial Seed Selection for Frequency
Domain Blind Speech Separation,” in Interspeech 2011, 2011.
mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “On Initial Seed Selection for
Frequency Domain Blind Speech Separation.” Interspeech 2011, 2011.
short: 'D.H. Tran Vu, R. Haeb-Umbach, in: Interspeech 2011, 2011.'
date_created: 2019-07-12T05:30:38Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/TrHa11.pdf
oa: '1'
publication: Interspeech 2011
status: public
title: On Initial Seed Selection for Frequency Domain Blind Speech Separation
type: conference
user_id: '44006'
year: '2011'
...
---
_id: '11945'
citation:
ama: Kolossa D, Haeb-Umbach R, eds. Robust Speech Recognition of Uncertain or
Missing Data --- Theory and Applications. Springer; 2011.
apa: Kolossa, D., & Haeb-Umbach, R. (Eds.). (2011). Robust Speech Recognition
of Uncertain or Missing Data --- Theory and Applications. Springer.
bibtex: '@book{Kolossa_Haeb-Umbach_2011, title={Robust Speech Recognition of Uncertain
or Missing Data --- Theory and Applications}, publisher={Springer}, year={2011}
}'
chicago: Kolossa, Dorothea, and Reinhold Haeb-Umbach, eds. Robust Speech Recognition
of Uncertain or Missing Data --- Theory and Applications. Springer, 2011.
ieee: D. Kolossa and R. Haeb-Umbach, Eds., Robust Speech Recognition of Uncertain
or Missing Data --- Theory and Applications. Springer, 2011.
mla: Kolossa, Dorothea, and Reinhold Haeb-Umbach, editors. Robust Speech Recognition
of Uncertain or Missing Data --- Theory and Applications. Springer, 2011.
short: D. Kolossa, R. Haeb-Umbach, eds., Robust Speech Recognition of Uncertain
or Missing Data --- Theory and Applications, Springer, 2011.
date_created: 2019-07-12T05:31:17Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
editor:
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.springer.com/engineering/signals/book/978-3-642-21316-8?detailsPage=authorsAndEditors
oa: '1'
publisher: Springer
status: public
title: Robust Speech Recognition of Uncertain or Missing Data --- Theory and Applications
type: book_editor
user_id: '44006'
year: '2011'
...
---
_id: '11889'
abstract:
- lang: eng
text: In this paper we propose to jointly consider Segmental Dynamic Time Warping
and distance clustering for the unsupervised learning of acoustic events. As a
result, the computational complexity increases only linearly with the dababase
size compared to a quadratic increase in a sequential setup, where all pairwise
SDTW distances between segments are computed prior to clustering. Further, we
discuss options for seed value selection for clustering and show that drawing
seeds with a probability proportional to the distance from the already drawn seeds,
known as K-means++ clustering, results in a significantly higher probability of
finding representatives of each of the underlying classes, compared to the commonly
used draws from a uniform distribution. Experiments are performed on an acoustic
event classification and an isolated digit recognition task, where on the latter
the final word accuracy approaches that of supervised training.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Markus
full_name: Bartek, Markus
last_name: Bartek
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Bartek M, Haeb-Umbach R. Unsupervised learning of acoustic
events using dynamic time warping and hierarchical K-means++ clustering. In: Interspeech
2011. ; 2011.'
apa: Schmalenstroeer, J., Bartek, M., & Haeb-Umbach, R. (2011). Unsupervised
learning of acoustic events using dynamic time warping and hierarchical K-means++
clustering. Interspeech 2011.
bibtex: '@inproceedings{Schmalenstroeer_Bartek_Haeb-Umbach_2011, title={Unsupervised
learning of acoustic events using dynamic time warping and hierarchical K-means++
clustering}, booktitle={Interspeech 2011}, author={Schmalenstroeer, Joerg and
Bartek, Markus and Haeb-Umbach, Reinhold}, year={2011} }'
chicago: Schmalenstroeer, Joerg, Markus Bartek, and Reinhold Haeb-Umbach. “Unsupervised
Learning of Acoustic Events Using Dynamic Time Warping and Hierarchical K-Means++
Clustering.” In Interspeech 2011, 2011.
ieee: J. Schmalenstroeer, M. Bartek, and R. Haeb-Umbach, “Unsupervised learning
of acoustic events using dynamic time warping and hierarchical K-means++ clustering,”
2011.
mla: Schmalenstroeer, Joerg, et al. “Unsupervised Learning of Acoustic Events Using
Dynamic Time Warping and Hierarchical K-Means++ Clustering.” Interspeech 2011,
2011.
short: 'J. Schmalenstroeer, M. Bartek, R. Haeb-Umbach, in: Interspeech 2011, 2011.'
date_created: 2019-07-12T05:30:13Z
date_updated: 2023-10-26T08:10:44Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/ScBaHa11-2.pdf
oa: '1'
publication: Interspeech 2011
quality_controlled: '1'
status: public
title: Unsupervised learning of acoustic events using dynamic time warping and hierarchical
K-means++ clustering
type: conference
user_id: '460'
year: '2011'
...
---
_id: '11896'
abstract:
- lang: eng
text: In this paper we propose a procedure for estimating the geometric configuration
of an arbitrary acoustic sensor placement. It determines the position and the
orientation of microphone arrays in 2D while locating a source by direction-of-arrival
(DoA) estimation. Neither artificial calibration signals nor unnatural user activity
are required. The problem of scale indeterminacy inherent to DoA-only observations
is solved by adding time difference of arrival (TDOA) measurements. The geometry
calibration method is numerically stable and delivers precise results in moderately
reverberated rooms. Simulation results are confirmed by laboratory experiments.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Florian
full_name: Jacob, Florian
last_name: Jacob
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Marius
full_name: Hennecke, Marius
last_name: Hennecke
- first_name: Gernot A.
full_name: Fink, Gernot A.
last_name: Fink
citation:
ama: 'Schmalenstroeer J, Jacob F, Haeb-Umbach R, Hennecke M, Fink GA. Unsupervised
Geometry Calibration of Acoustic Sensor Networks Using Source Correspondences.
In: Interspeech 2011. ; 2011.'
apa: Schmalenstroeer, J., Jacob, F., Haeb-Umbach, R., Hennecke, M., & Fink,
G. A. (2011). Unsupervised Geometry Calibration of Acoustic Sensor Networks Using
Source Correspondences. Interspeech 2011.
bibtex: '@inproceedings{Schmalenstroeer_Jacob_Haeb-Umbach_Hennecke_Fink_2011, title={Unsupervised
Geometry Calibration of Acoustic Sensor Networks Using Source Correspondences},
booktitle={Interspeech 2011}, author={Schmalenstroeer, Joerg and Jacob, Florian
and Haeb-Umbach, Reinhold and Hennecke, Marius and Fink, Gernot A.}, year={2011}
}'
chicago: Schmalenstroeer, Joerg, Florian Jacob, Reinhold Haeb-Umbach, Marius Hennecke,
and Gernot A. Fink. “Unsupervised Geometry Calibration of Acoustic Sensor Networks
Using Source Correspondences.” In Interspeech 2011, 2011.
ieee: J. Schmalenstroeer, F. Jacob, R. Haeb-Umbach, M. Hennecke, and G. A. Fink,
“Unsupervised Geometry Calibration of Acoustic Sensor Networks Using Source Correspondences,”
2011.
mla: Schmalenstroeer, Joerg, et al. “Unsupervised Geometry Calibration of Acoustic
Sensor Networks Using Source Correspondences.” Interspeech 2011, 2011.
short: 'J. Schmalenstroeer, F. Jacob, R. Haeb-Umbach, M. Hennecke, G.A. Fink, in:
Interspeech 2011, 2011.'
date_created: 2019-07-12T05:30:21Z
date_updated: 2023-10-26T08:10:28Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/ScJaHaHeFi11.pdf
oa: '1'
publication: Interspeech 2011
quality_controlled: '1'
status: public
title: Unsupervised Geometry Calibration of Acoustic Sensor Networks Using Source
Correspondences
type: conference
user_id: '460'
year: '2011'
...
---
_id: '9456'
abstract:
- lang: eng
text: In this paper we present our experimental results about classifying audio
data into broad acoustic categories. The reverberated sound samples from indoor
recordings are grouped into four classes, namely speech, music, acoustic events
and noise. We investigated a total of 188 acoustic features and achieved for the
best configuration a classification accuracy better than 98\%. This was achieved
by a 42-dimensional feature vector consisting of Mel-Frequency Cepstral Coefficients,
an autocorrelation feature and so-called track features that measure the length
of ''traces'' of high energy in the spectrogram. We also found a 4-feature configuration
with a classification rate of about 90\% allowing for broad acoustic category
classification with low computational effort.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Markus
full_name: Bartek, Markus
last_name: Bartek
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Bartek M, Haeb-Umbach R. Investigations into Features for
Robust Classification into Broad Acoustic Categories. In: 37. Deutsche Jahrestagung
Fuer Akustik (DAGA 2011). ; 2011.'
apa: Schmalenstroeer, J., Bartek, M., & Haeb-Umbach, R. (2011). Investigations
into Features for Robust Classification into Broad Acoustic Categories. 37.
Deutsche Jahrestagung Fuer Akustik (DAGA 2011).
bibtex: '@inproceedings{Schmalenstroeer_Bartek_Haeb-Umbach_2011, title={Investigations
into Features for Robust Classification into Broad Acoustic Categories}, booktitle={37.
Deutsche Jahrestagung fuer Akustik (DAGA 2011)}, author={Schmalenstroeer, Joerg
and Bartek, Markus and Haeb-Umbach, Reinhold}, year={2011} }'
chicago: Schmalenstroeer, Joerg, Markus Bartek, and Reinhold Haeb-Umbach. “Investigations
into Features for Robust Classification into Broad Acoustic Categories.” In 37.
Deutsche Jahrestagung Fuer Akustik (DAGA 2011), 2011.
ieee: J. Schmalenstroeer, M. Bartek, and R. Haeb-Umbach, “Investigations into Features
for Robust Classification into Broad Acoustic Categories,” 2011.
mla: Schmalenstroeer, Joerg, et al. “Investigations into Features for Robust Classification
into Broad Acoustic Categories.” 37. Deutsche Jahrestagung Fuer Akustik (DAGA
2011), 2011.
short: 'J. Schmalenstroeer, M. Bartek, R. Haeb-Umbach, in: 37. Deutsche Jahrestagung
Fuer Akustik (DAGA 2011), 2011.'
date_created: 2019-04-25T14:36:02Z
date_updated: 2023-10-26T08:15:44Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2011/ScBaHa11-1.pdf
oa: '1'
publication: 37. Deutsche Jahrestagung fuer Akustik (DAGA 2011)
quality_controlled: '1'
status: public
title: Investigations into Features for Robust Classification into Broad Acoustic
Categories
type: conference
user_id: '460'
year: '2011'
...
---
_id: '11726'
abstract:
- lang: eng
text: 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.
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Sven
full_name: Peschke, Sven
last_name: Peschke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Bevermeier M, Walter O, Peschke S, Haeb-Umbach R. Barometric height estimation
combined with map-matching in a loosely-coupled Kalman-filter. In: 7th Workshop
on Positioning Navigation and Communication (WPNC 2010). ; 2010:128-134. doi:10.1109/WPNC.2010.5650745'
apa: Bevermeier, M., Walter, O., Peschke, S., & Haeb-Umbach, R. (2010). Barometric
height estimation combined with map-matching in a loosely-coupled Kalman-filter.
In 7th Workshop on Positioning Navigation and Communication (WPNC 2010)
(pp. 128–134). https://doi.org/10.1109/WPNC.2010.5650745
bibtex: '@inproceedings{Bevermeier_Walter_Peschke_Haeb-Umbach_2010, title={Barometric
height estimation combined with map-matching in a loosely-coupled Kalman-filter},
DOI={10.1109/WPNC.2010.5650745},
booktitle={7th Workshop on Positioning Navigation and Communication (WPNC 2010)},
author={Bevermeier, Maik and Walter, Oliver and Peschke, Sven and Haeb-Umbach,
Reinhold}, year={2010}, pages={128–134} }'
chicago: Bevermeier, Maik, Oliver Walter, Sven Peschke, and Reinhold Haeb-Umbach.
“Barometric Height Estimation Combined with Map-Matching in a Loosely-Coupled
Kalman-Filter.” In 7th Workshop on Positioning Navigation and Communication
(WPNC 2010), 128–34, 2010. https://doi.org/10.1109/WPNC.2010.5650745.
ieee: M. Bevermeier, O. Walter, S. Peschke, and R. Haeb-Umbach, “Barometric height
estimation combined with map-matching in a loosely-coupled Kalman-filter,” in
7th Workshop on Positioning Navigation and Communication (WPNC 2010), 2010,
pp. 128–134.
mla: Bevermeier, Maik, et al. “Barometric Height Estimation Combined with Map-Matching
in a Loosely-Coupled Kalman-Filter.” 7th Workshop on Positioning Navigation
and Communication (WPNC 2010), 2010, pp. 128–34, doi:10.1109/WPNC.2010.5650745.
short: 'M. Bevermeier, O. Walter, S. Peschke, R. Haeb-Umbach, in: 7th Workshop on
Positioning Navigation and Communication (WPNC 2010), 2010, pp. 128–134.'
date_created: 2019-07-12T05:27:04Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/WPNC.2010.5650745
keyword:
- altitude measurement unit
- barometers
- barometric altimeter
- barometric error Kalman filter
- barometric height estimation
- direct cosine matrix
- global positioning system
- Global Positioning System
- GPS device
- height information
- height measurement
- inertial measurement unit
- Kalman filters
- loosely-coupled error state space Kalman filter
- loosely-coupled Kalman-filter
- map matching
- robust information
- robust location estimation
- sensor fusion
- topographic map information
- vertical user position
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/BeWaPeHa10.pdf
oa: '1'
page: 128-134
publication: 7th Workshop on Positioning Navigation and Communication (WPNC 2010)
status: public
title: Barometric height estimation combined with map-matching in a loosely-coupled
Kalman-filter
type: conference
user_id: '44006'
year: '2010'
...
---
_id: '11846'
abstract:
- lang: eng
text: 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.
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. Model-Based Feature Enhancement for Reverberant Speech
Recognition. IEEE Transactions on Audio, Speech, and Language Processing.
2010;18(7):1692-1707. doi:10.1109/TASL.2010.2049684
apa: Krueger, A., & Haeb-Umbach, R. (2010). Model-Based Feature Enhancement
for Reverberant Speech Recognition. IEEE Transactions on Audio, Speech, and
Language Processing, 18(7), 1692–1707. https://doi.org/10.1109/TASL.2010.2049684
bibtex: '@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement
for Reverberant Speech Recognition}, volume={18}, DOI={10.1109/TASL.2010.2049684},
number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707}
}'
chicago: 'Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing 18, no. 7 (2010): 1692–1707. https://doi.org/10.1109/TASL.2010.2049684.'
ieee: A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant
Speech Recognition,” IEEE Transactions on Audio, Speech, and Language Processing,
vol. 18, no. 7, pp. 1692–1707, 2010.
mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
for Reverberant Speech Recognition.” IEEE Transactions on Audio, Speech, and
Language Processing, vol. 18, no. 7, 2010, pp. 1692–707, doi:10.1109/TASL.2010.2049684.
short: A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 18 (2010) 1692–1707.
date_created: 2019-07-12T05:29:23Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2049684
intvolume: ' 18'
issue: '7'
keyword:
- ASR
- AURORA5 database
- automatic speech recognition
- Bayesian inference
- belief networks
- CMLLR
- computational complexity
- constrained maximum likelihood linear regression
- least mean squares methods
- LMPSC computation
- logarithmic Mel power spectrum
- maximum likelihood estimation
- Mel frequency cepstral coefficients
- MFCC feature vectors
- microphone signal
- minimum mean square error estimation
- model-based feature enhancement
- regression analysis
- reverberant speech recognition
- reverberation
- RIR energy
- room impulse response
- speech recognition
- stochastic observation model
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf
oa: '1'
page: 1692-1707
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Model-Based Feature Enhancement for Reverberant Speech Recognition
type: journal_article
user_id: '44006'
volume: 18
year: '2010'
...
---
_id: '11857'
abstract:
- lang: eng
text: Traditionally, ASR systems are based on hidden Markov models with Gaussian
mixtures modelling the state-conditioned feature distribution. The inherent assumption
of conditional independence, stating that a feature's likelihood solely depends
on the current HMM state, makes the search computationally tractable, nevertheless
has also been identified to be a major reason for the lack of robustness of such
systems. Linear dynamic models have been proposed to overcome this weakness by
employing a hidden dynamic state process underlying the observed features. Though
performance of linear dynamic models on continuous speech/phone recognition tasks
has been shown to be superior to that of equivalent static models, this approach
still cannot compete with the established acoustic models. In this paper we consider
the combination of hidden Markov models based on Gaussian mixture densities (GMM-HMMs)
and linear dynamic models (LDMs) as the acoustic model for automatic speech recognition
systems. In doing so, the individual strengths of both models, i.e. the modelling
of long-term temporal dependencies by the GMM-HMM and the direct modelling of
statistical dependencies between consecutive feature vectors by the LDM, are exploited.
Phone classification experiments conducted on the TIMIT database indicate the
prospective use of this approach for the application to continuous speech recognition.
author:
- 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: 'Leutnant V, Haeb-Umbach R. Options for Modelling Temporal Statistical Dependencies
in an Acoustic Model for ASR. In: 36. Deutsche Jahrestagung Fuer Akustik (DAGA
2010). ; 2010.'
apa: Leutnant, V., & Haeb-Umbach, R. (2010). Options for Modelling Temporal
Statistical Dependencies in an Acoustic Model for ASR. In 36. Deutsche Jahrestagung
fuer Akustik (DAGA 2010).
bibtex: '@inproceedings{Leutnant_Haeb-Umbach_2010, title={Options for Modelling
Temporal Statistical Dependencies in an Acoustic Model for ASR}, booktitle={36.
Deutsche Jahrestagung fuer Akustik (DAGA 2010)}, author={Leutnant, Volker and
Haeb-Umbach, Reinhold}, year={2010} }'
chicago: Leutnant, Volker, and Reinhold Haeb-Umbach. “Options for Modelling Temporal
Statistical Dependencies in an Acoustic Model for ASR.” In 36. Deutsche Jahrestagung
Fuer Akustik (DAGA 2010), 2010.
ieee: V. Leutnant and R. Haeb-Umbach, “Options for Modelling Temporal Statistical
Dependencies in an Acoustic Model for ASR,” in 36. Deutsche Jahrestagung fuer
Akustik (DAGA 2010), 2010.
mla: Leutnant, Volker, and Reinhold Haeb-Umbach. “Options for Modelling Temporal
Statistical Dependencies in an Acoustic Model for ASR.” 36. Deutsche Jahrestagung
Fuer Akustik (DAGA 2010), 2010.
short: 'V. Leutnant, R. Haeb-Umbach, in: 36. Deutsche Jahrestagung Fuer Akustik
(DAGA 2010), 2010.'
date_created: 2019-07-12T05:29:36Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/LeHa10-1.pdf
oa: '1'
publication: 36. Deutsche Jahrestagung fuer Akustik (DAGA 2010)
status: public
title: Options for Modelling Temporal Statistical Dependencies in an Acoustic Model
for ASR
type: conference
user_id: '44006'
year: '2010'
...
---
_id: '11858'
abstract:
- lang: eng
text: Linear dynamic models (LDMs) have been shown to be a viable alternative to
hidden Markov models (HMMs) on small-vocabulary recognition tasks, such as phone
classification. In this paper we investigate various statistical model combination
approaches for a hybrid HMM-LDM recognizer, resulting in a phone classification
performance that outperforms the best individual classifier. Further, we report
on continuous speech recognition experiments on the AURORA4 corpus, where the
model combination is carried out on wordgraph rescoring. While the hybrid system
improves the HMM system in the case of monophone HMMs, the performance of the
triphone HMM model could not be improved by monophone LDMs, asking for the need
to introduce context-dependency also in the LDM model inventory.
author:
- 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: 'Leutnant V, Haeb-Umbach R. On the Exploitation of Hidden Markov Models and
Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous Speech Recognition.
In: Interspeech 2010. ; 2010.'
apa: Leutnant, V., & Haeb-Umbach, R. (2010). On the Exploitation of Hidden Markov
Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous
Speech Recognition. In Interspeech 2010.
bibtex: '@inproceedings{Leutnant_Haeb-Umbach_2010, title={On the Exploitation of
Hidden Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture
for Continuous Speech Recognition}, booktitle={Interspeech 2010}, author={Leutnant,
Volker and Haeb-Umbach, Reinhold}, year={2010} }'
chicago: Leutnant, Volker, and Reinhold Haeb-Umbach. “On the Exploitation of Hidden
Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous
Speech Recognition.” In Interspeech 2010, 2010.
ieee: V. Leutnant and R. Haeb-Umbach, “On the Exploitation of Hidden Markov Models
and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous Speech
Recognition,” in Interspeech 2010, 2010.
mla: Leutnant, Volker, and Reinhold Haeb-Umbach. “On the Exploitation of Hidden
Markov Models and Linear Dynamic Models in a Hybrid Decoder Architecture for Continuous
Speech Recognition.” Interspeech 2010, 2010.
short: 'V. Leutnant, R. Haeb-Umbach, in: Interspeech 2010, 2010.'
date_created: 2019-07-12T05:29:37Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/LeHa10-2.pdf
oa: '1'
publication: Interspeech 2010
status: public
title: On the Exploitation of Hidden Markov Models and Linear Dynamic Models in a
Hybrid Decoder Architecture for Continuous Speech Recognition
type: conference
user_id: '44006'
year: '2010'
...
---
_id: '11887'
abstract:
- lang: eng
text: We describe an algorithm that performs regularized non-negative matrix factorization
(NMF) to find independent components in non-negative data. Previous techniques
proposed for this purpose require the data to be grounded, with support that goes
down to 0 along each dimension. In our work, this requirement is eliminated. Based
on it, we present a technique to find a low-dimensional decomposition of spectrograms
by casting it as a problem of discovering independent non-negative components
from it. The algorithm itself is implemented as regularized non-negative matrix
factorization (NMF). Unlike other ICA algorithms, this algorithm computes the
mixing matrix rather than an unmixing matrix. This algorithm provides a better
decomposition than standard NMF when the underlying sources are independent. It
makes better use of additional observation streams than previous non-negative
ICA algorithms.
author:
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
- first_name: Kevin W.
full_name: Wilson, Kevin W.
last_name: Wilson
- 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: 'Raj B, Wilson KW, Krueger A, Haeb-Umbach R. Ungrounded Independent Non-Negative
Factor Analysis. In: Interspeech 2010. ; 2010.'
apa: Raj, B., Wilson, K. W., Krueger, A., & Haeb-Umbach, R. (2010). Ungrounded
Independent Non-Negative Factor Analysis. In Interspeech 2010.
bibtex: '@inproceedings{Raj_Wilson_Krueger_Haeb-Umbach_2010, title={Ungrounded Independent
Non-Negative Factor Analysis}, booktitle={Interspeech 2010}, author={Raj, Bhiksha
and Wilson, Kevin W. and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}
}'
chicago: Raj, Bhiksha, Kevin W. Wilson, Alexander Krueger, and Reinhold Haeb-Umbach.
“Ungrounded Independent Non-Negative Factor Analysis.” In Interspeech 2010,
2010.
ieee: B. Raj, K. W. Wilson, A. Krueger, and R. Haeb-Umbach, “Ungrounded Independent
Non-Negative Factor Analysis,” in Interspeech 2010, 2010.
mla: Raj, Bhiksha, et al. “Ungrounded Independent Non-Negative Factor Analysis.”
Interspeech 2010, 2010.
short: 'B. Raj, K.W. Wilson, A. Krueger, R. Haeb-Umbach, in: Interspeech 2010, 2010.'
date_created: 2019-07-12T05:30:10Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/RaWiKrHa10.pdf
oa: '1'
publication: Interspeech 2010
status: public
title: Ungrounded Independent Non-Negative Factor Analysis
type: conference
user_id: '44006'
year: '2010'
...