---
_id: '9879'
abstract:
- lang: eng
text: Application of prognostics and health management (PHM) in the field of Proton
Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing
the reliability and availability of these systems. Though a lot of work is currently
being conducted to develop PHM systems for fuel cells, various challenges have
been encountered including the self-healing effect after characterization as well
as accelerated degradation due to dynamic loading, all which make RUL predictions
a difficult task. In this study, a prognostic approach based on adaptive particle
filter algorithm is proposed. The novelty of the proposed method lies in the introduction
of a self-healing factor after each characterization and the adaption of the degradation
model parameters to fit to the changing degradation trend. An ensemble of five
different state models based on weighted mean is then developed. The results show
that the method is effective in estimating the remaining useful life of PEM fuel
cells, with majority of the predictions falling within 5\% error. The method was
employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the
winner of the RUL category of the challenge.
author:
- first_name: 'James Kuria '
full_name: 'Kimotho, James Kuria '
last_name: Kimotho
- first_name: Tobias
full_name: Meyer, Tobias
last_name: Meyer
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Kimotho JK, Meyer T, Sextro W. PEM fuel cell prognostics using particle filter
with model parameter adaptation. In: Prognostics and Health Management (PHM),
2014 IEEE Conference On. ; 2014:1-6. doi:10.1109/ICPHM.2014.7036406'
apa: Kimotho, J. K., Meyer, T., & Sextro, W. (2014). PEM fuel cell prognostics
using particle filter with model parameter adaptation. In Prognostics and Health
Management (PHM), 2014 IEEE Conference on (pp. 1–6). https://doi.org/10.1109/ICPHM.2014.7036406
bibtex: '@inproceedings{Kimotho_Meyer_Sextro_2014, title={PEM fuel cell prognostics
using particle filter with model parameter adaptation}, DOI={10.1109/ICPHM.2014.7036406},
booktitle={Prognostics and Health Management (PHM), 2014 IEEE Conference on},
author={Kimotho, James Kuria and Meyer, Tobias and Sextro, Walter}, year={2014},
pages={1–6} }'
chicago: Kimotho, James Kuria , Tobias Meyer, and Walter Sextro. “PEM Fuel Cell
Prognostics Using Particle Filter with Model Parameter Adaptation.” In Prognostics
and Health Management (PHM), 2014 IEEE Conference On, 1–6, 2014. https://doi.org/10.1109/ICPHM.2014.7036406.
ieee: J. K. Kimotho, T. Meyer, and W. Sextro, “PEM fuel cell prognostics using particle
filter with model parameter adaptation,” in Prognostics and Health Management
(PHM), 2014 IEEE Conference on, 2014, pp. 1–6.
mla: Kimotho, James Kuria, et al. “PEM Fuel Cell Prognostics Using Particle Filter
with Model Parameter Adaptation.” Prognostics and Health Management (PHM),
2014 IEEE Conference On, 2014, pp. 1–6, doi:10.1109/ICPHM.2014.7036406.
short: 'J.K. Kimotho, T. Meyer, W. Sextro, in: Prognostics and Health Management
(PHM), 2014 IEEE Conference On, 2014, pp. 1–6.'
date_created: 2019-05-20T13:11:02Z
date_updated: 2019-05-20T13:12:27Z
department:
- _id: '151'
doi: 10.1109/ICPHM.2014.7036406
keyword:
- ageing
- particle filtering (numerical methods)
- proton exchange membrane fuel cells
- remaining life assessment
- PEM fuel cell prognostics
- PHM
- RUL predictions
- accelerated degradation
- adaptive particle filter algorithm
- dynamic loading
- model parameter adaptation
- prognostics and health management
- proton exchange membrane fuel cells
- remaining useful life estimation
- self-healing effect
- Adaptation models
- Data models
- Degradation
- Estimation
- Fuel cells
- Mathematical model
- Prognostics and health management
language:
- iso: eng
page: 1-6
publication: Prognostics and Health Management (PHM), 2014 IEEE Conference on
status: public
title: PEM fuel cell prognostics using particle filter with model parameter adaptation
type: conference
user_id: '55222'
year: '2014'
...
---
_id: '11898'
abstract:
- lang: eng
text: Abstract In this paper we present an approach for synchronizing a wireless
acoustic sensor network using a two-stage procedure. First the clock frequency
and phase differences between pairs of nodes are estimated employing a two-way
message exchange protocol. The estimates are further improved in a Kalman filter
with a dedicated observation error model. In the second stage network-wide synchronization
is achieved by means of a gossiping algorithm which estimates the average clock
frequency and phase of the sensor nodes. These averages are viewed as frequency
and phase of a virtual master clock, to which the clocks of the sensor nodes have
to be adjusted. The amount of adjustment is computed in a specific control loop.
While these steps are done in software, the actual sampling rate correction is
carried out in hardware by using an adjustable frequency synthesizer. Experimental
results obtained from hardware devices and software simulations of large scale
networks are presented.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Patrick
full_name: Jebramcik, Patrick
last_name: Jebramcik
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Schmalenstroeer J, Jebramcik P, Haeb-Umbach R. A combined hardware-software
approach for acoustic sensor network synchronization . Signal Processing.
2014;(0). doi:http://dx.doi.org/10.1016/j.sigpro.2014.06.030
apa: Schmalenstroeer, J., Jebramcik, P., & Haeb-Umbach, R. (2014). A combined
hardware-software approach for acoustic sensor network synchronization . Signal
Processing, 0. http://dx.doi.org/10.1016/j.sigpro.2014.06.030
bibtex: '@article{Schmalenstroeer_Jebramcik_Haeb-Umbach_2014, title={A combined
hardware-software approach for acoustic sensor network synchronization }, DOI={http://dx.doi.org/10.1016/j.sigpro.2014.06.030},
number={0}, journal={Signal Processing}, author={Schmalenstroeer, Joerg and Jebramcik,
Patrick and Haeb-Umbach, Reinhold}, year={2014} }'
chicago: Schmalenstroeer, Joerg, Patrick Jebramcik, and Reinhold Haeb-Umbach. “A
Combined Hardware-Software Approach for Acoustic Sensor Network Synchronization
.” Signal Processing, no. 0 (2014). http://dx.doi.org/10.1016/j.sigpro.2014.06.030.
ieee: 'J. Schmalenstroeer, P. Jebramcik, and R. Haeb-Umbach, “A combined hardware-software
approach for acoustic sensor network synchronization ,” Signal Processing,
no. 0, p., 2014, doi: http://dx.doi.org/10.1016/j.sigpro.2014.06.030.'
mla: Schmalenstroeer, Joerg, et al. “A Combined Hardware-Software Approach for Acoustic
Sensor Network Synchronization .” Signal Processing, no. 0, 2014, p., doi:http://dx.doi.org/10.1016/j.sigpro.2014.06.030.
short: J. Schmalenstroeer, P. Jebramcik, R. Haeb-Umbach, Signal Processing (2014).
date_created: 2019-07-12T05:30:23Z
date_updated: 2023-10-26T08:11:22Z
department:
- _id: '54'
doi: http://dx.doi.org/10.1016/j.sigpro.2014.06.030
issue: '0'
keyword:
- Gossip algorithm
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://www.sciencedirect.com/science/article/pii/S0165168414002990
oa: '1'
page: ' - '
publication: Signal Processing
publication_identifier:
issn:
- 0165-1684
quality_controlled: '1'
status: public
title: 'A combined hardware-software approach for acoustic sensor network synchronization '
type: journal_article
user_id: '460'
year: '2014'
...
---
_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: '11917'
abstract:
- lang: eng
text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich
exploits both temporal and spectral correlations of speech. To this end, the SPP
estimation is formulated as the posterior probability estimation of the states
of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
to decode the 2D-HMM which is based on the turbo principle. The experimental results
show that indeed the SPP estimates improve from iteration to iteration, and further
clearly outperform another state-of-the-art SPP estimation algorithm.
author:
- first_name: Dang Hai Tran
full_name: Vu, Dang Hai Tran
last_name: Vu
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and
spectral correlations in speech presence probability estimation. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:863-867.
doi:10.1109/ICASSP.2013.6637771'
apa: Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation.
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771
bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
exploiting temporal and spectral correlations in speech presence probability estimation},
DOI={10.1109/ICASSP.2013.6637771},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013},
pages={863–867} }'
chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle
for Exploiting Temporal and Spectral Correlations in Speech Presence Probability
Estimation.” In 38th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2013), 863–67, 2013. https://doi.org/10.1109/ICASSP.2013.6637771.
ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation,”
in 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 2013, pp. 863–867.
mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for
Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.”
38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2013), 2013, pp. 863–67, doi:10.1109/ICASSP.2013.6637771.
short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.'
date_created: 2019-07-12T05:30:45Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637771
keyword:
- correlation methods
- estimation theory
- hidden Markov models
- iterative methods
- probability
- spectral analysis
- speech processing
- 2D HMM
- SPP estimates
- iterative algorithm
- posterior probability estimation
- spectral correlation
- speech presence probability estimation
- state-of-the-art SPP estimation algorithm
- temporal correlation
- turbo principle
- two-dimensional hidden Markov model
- Correlation
- Decoding
- Estimation
- Iterative decoding
- Noise
- Speech
- Vectors
language:
- iso: eng
page: 863-867
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
status: public
title: Using the turbo principle for exploiting temporal and spectral correlations
in speech presence probability estimation
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '46396'
abstract:
- lang: eng
text: The steady supply of new optimization methods makes the algorithm selection
problem (ASP) an increasingly pressing and challenging task, specially for real-world
black-box optimization problems. The introduced approach considers the ASP as
a cost-sensitive classification task which is based on Exploratory Landscape Analysis.
Low-level features gathered by systematic sampling of the function on the feasible
set are used to predict a well-performing algorithm out of a given portfolio.
Example-specific label costs are defined by the expected runtime of each candidate
algorithm. We use one-sided support vector regression to solve this learning problem.
The approach is illustrated by means of the optimization problems and algorithms
of the BBOB’09/10 workshop.
author:
- first_name: Bernd
full_name: Bischl, Bernd
last_name: Bischl
- first_name: Olaf
full_name: Mersmann, Olaf
last_name: Mersmann
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: Mike
full_name: Preuß, Mike
last_name: Preuß
citation:
ama: 'Bischl B, Mersmann O, Trautmann H, Preuß M. Algorithm Selection Based on Exploratory
Landscape Analysis and Cost-Sensitive Learning. In: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation. GECCO ’12. Association
for Computing Machinery; 2012:313–320. doi:10.1145/2330163.2330209'
apa: Bischl, B., Mersmann, O., Trautmann, H., & Preuß, M. (2012). Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.
Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. https://doi.org/10.1145/2330163.2330209
bibtex: '@inproceedings{Bischl_Mersmann_Trautmann_Preuß_2012, place={New York, NY,
USA}, series={GECCO ’12}, title={Algorithm Selection Based on Exploratory Landscape
Analysis and Cost-Sensitive Learning}, DOI={10.1145/2330163.2330209},
booktitle={Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation}, publisher={Association for Computing Machinery}, author={Bischl,
Bernd and Mersmann, Olaf and Trautmann, Heike and Preuß, Mike}, year={2012}, pages={313–320},
collection={GECCO ’12} }'
chicago: 'Bischl, Bernd, Olaf Mersmann, Heike Trautmann, and Mike Preuß. “Algorithm
Selection Based on Exploratory Landscape Analysis and Cost-Sensitive Learning.”
In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation,
313–320. GECCO ’12. New York, NY, USA: Association for Computing Machinery, 2012.
https://doi.org/10.1145/2330163.2330209.'
ieee: 'B. Bischl, O. Mersmann, H. Trautmann, and M. Preuß, “Algorithm Selection
Based on Exploratory Landscape Analysis and Cost-Sensitive Learning,” in Proceedings
of the 14th Annual Conference on Genetic and Evolutionary Computation, 2012,
pp. 313–320, doi: 10.1145/2330163.2330209.'
mla: Bischl, Bernd, et al. “Algorithm Selection Based on Exploratory Landscape Analysis
and Cost-Sensitive Learning.” Proceedings of the 14th Annual Conference on
Genetic and Evolutionary Computation, Association for Computing Machinery,
2012, pp. 313–320, doi:10.1145/2330163.2330209.
short: 'B. Bischl, O. Mersmann, H. Trautmann, M. Preuß, in: Proceedings of the 14th
Annual Conference on Genetic and Evolutionary Computation, Association for Computing
Machinery, New York, NY, USA, 2012, pp. 313–320.'
date_created: 2023-08-04T15:51:56Z
date_updated: 2023-10-16T13:48:48Z
department:
- _id: '34'
- _id: '819'
doi: 10.1145/2330163.2330209
keyword:
- machine learning
- exploratory landscape analysis
- fitness landscape
- benchmarking
- evolutionary optimization
- bbob test set
- algorithm selection
language:
- iso: eng
page: 313–320
place: New York, NY, USA
publication: Proceedings of the 14th Annual Conference on Genetic and Evolutionary
Computation
publication_identifier:
isbn:
- '9781450311779'
publisher: Association for Computing Machinery
series_title: GECCO ’12
status: public
title: Algorithm Selection Based on Exploratory Landscape Analysis and Cost-Sensitive
Learning
type: conference
user_id: '15504'
year: '2012'
...
---
_id: '2990'
author:
- first_name: Marcel R.
full_name: Ackermann, Marcel R.
last_name: Ackermann
- first_name: Johannes
full_name: Blömer, Johannes
id: '23'
last_name: Blömer
- first_name: Christian
full_name: Sohler, Christian
last_name: Sohler
citation:
ama: Ackermann MR, Blömer J, Sohler C. Clustering for Metric and Nonmetric Distance
Measures. ACM Trans Algorithms. 2010;(4):59:1--59:26. doi:10.1145/1824777.1824779
apa: Ackermann, M. R., Blömer, J., & Sohler, C. (2010). Clustering for Metric
and Nonmetric Distance Measures. ACM Trans. Algorithms, (4), 59:1--59:26.
https://doi.org/10.1145/1824777.1824779
bibtex: '@article{Ackermann_Blömer_Sohler_2010, title={Clustering for Metric and
Nonmetric Distance Measures}, DOI={10.1145/1824777.1824779},
number={4}, journal={ACM Trans. Algorithms}, author={Ackermann, Marcel R. and
Blömer, Johannes and Sohler, Christian}, year={2010}, pages={59:1--59:26} }'
chicago: 'Ackermann, Marcel R., Johannes Blömer, and Christian Sohler. “Clustering
for Metric and Nonmetric Distance Measures.” ACM Trans. Algorithms, no.
4 (2010): 59:1--59:26. https://doi.org/10.1145/1824777.1824779.'
ieee: M. R. Ackermann, J. Blömer, and C. Sohler, “Clustering for Metric and Nonmetric
Distance Measures,” ACM Trans. Algorithms, no. 4, pp. 59:1--59:26, 2010.
mla: Ackermann, Marcel R., et al. “Clustering for Metric and Nonmetric Distance
Measures.” ACM Trans. Algorithms, no. 4, 2010, pp. 59:1--59:26, doi:10.1145/1824777.1824779.
short: M.R. Ackermann, J. Blömer, C. Sohler, ACM Trans. Algorithms (2010) 59:1--59:26.
date_created: 2018-06-05T07:52:41Z
date_updated: 2022-01-06T06:58:50Z
department:
- _id: '64'
doi: 10.1145/1824777.1824779
issue: '4'
keyword:
- k-means clustering
- k-median clustering
- Approximation algorithm
- Bregman divergences
- Itakura-Saito divergence
- Kullback-Leibler divergence
- Mahalanobis distance
- random sampling
page: 59:1--59:26
publication: ACM Trans. Algorithms
publication_identifier:
issn:
- 1549-6325
publication_status: published
status: public
title: Clustering for Metric and Nonmetric Distance Measures
type: journal_article
user_id: '25078'
year: '2010'
...
---
_id: '11913'
abstract:
- lang: eng
text: In this paper we propose to employ directional statistics in a complex vector
space to approach the problem of blind speech separation in the presence of spatially
correlated noise. We interpret the values of the short time Fourier transform
of the microphone signals to be draws from a mixture of complex Watson distributions,
a probabilistic model which naturally accounts for spatial aliasing. The parameters
of the density are related to the a priori source probabilities, the power of
the sources and the transfer function ratios from sources to sensors. Estimation
formulas are derived for these parameters by employing the Expectation Maximization
(EM) algorithm. The E-step corresponds to the estimation of the source presence
probabilities for each time-frequency bin, while the M-step leads to a maximum
signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about
the source activity. Experimental results are reported for an implementation in
a generalized sidelobe canceller (GSC) like spatial beamforming configuration
for 3 speech sources with significant coherent noise in reverberant environments,
demonstrating the usefulness of the novel modeling framework.
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. Blind speech separation employing directional statistics
in an Expectation Maximization framework. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2010). ; 2010:241-244.
doi:10.1109/ICASSP.2010.5495994'
apa: Tran Vu, D. H., & Haeb-Umbach, R. (2010). Blind speech separation employing
directional statistics in an Expectation Maximization framework. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010) (pp. 241–244).
https://doi.org/10.1109/ICASSP.2010.5495994
bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2010, title={Blind speech separation
employing directional statistics in an Expectation Maximization framework}, DOI={10.1109/ICASSP.2010.5495994},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2010)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2010},
pages={241–244} }'
chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
Directional Statistics in an Expectation Maximization Framework.” In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 241–44,
2010. https://doi.org/10.1109/ICASSP.2010.5495994.
ieee: D. H. Tran Vu and R. Haeb-Umbach, “Blind speech separation employing directional
statistics in an Expectation Maximization framework,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010,
pp. 241–244.
mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
Directional Statistics in an Expectation Maximization Framework.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010,
pp. 241–44, doi:10.1109/ICASSP.2010.5495994.
short: 'D.H. Tran Vu, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.'
date_created: 2019-07-12T05:30:40Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2010.5495994
keyword:
- array signal processing
- blind source separation
- blind speech separation
- complex vector space
- complex Watson distribution
- directional statistics
- expectation-maximisation algorithm
- expectation maximization algorithm
- Fourier transform
- Fourier transforms
- generalized sidelobe canceller
- interference suppression
- maximum signal-to-noise ratio beamformer
- microphone signal
- probabilistic model
- spatial aliasing
- spatial beamforming configuration
- speech enhancement
- statistical distributions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/DaHa10-2.pdf
oa: '1'
page: 241-244
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2010)
status: public
title: Blind speech separation employing directional statistics in an Expectation
Maximization framework
type: conference
user_id: '44006'
year: '2010'
...
---
_id: '11723'
abstract:
- lang: eng
text: 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:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- 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, Peschke S, Haeb-Umbach R. Robust vehicle localization based
on multi-level sensor fusion and online parameter estimation. In: 6th Workshop
on Positioning Navigation and Communication (WPNC 2009). ; 2009:235-242. doi:10.1109/WPNC.2009.4907833'
apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation. In 6th
Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242).
https://doi.org/10.1109/WPNC.2009.4907833
bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle
localization based on multi-level sensor fusion and online parameter estimation},
DOI={10.1109/WPNC.2009.4907833},
booktitle={6th Workshop on Positioning Navigation and Communication (WPNC 2009)},
author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009},
pages={235–242} }'
chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Robust Vehicle
Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.”
In 6th Workshop on Positioning Navigation and Communication (WPNC 2009),
235–42, 2009. https://doi.org/10.1109/WPNC.2009.4907833.
ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation,” in 6th
Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp.
235–242.
mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level
Sensor Fusion and Online Parameter Estimation.” 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–42, doi:10.1109/WPNC.2009.4907833.
short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–242.'
date_created: 2019-07-12T05:27:01Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/WPNC.2009.4907833
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf
oa: '1'
page: 235-242
publication: 6th Workshop on Positioning Navigation and Communication (WPNC 2009)
status: public
title: Robust vehicle localization based on multi-level sensor fusion and online parameter
estimation
type: conference
user_id: '44006'
year: '2009'
...
---
_id: '11724'
abstract:
- lang: eng
text: 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:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- 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, Peschke S, Haeb-Umbach R. Joint Parameter Estimation and Tracking
in a Multi-Stage Kalman Filter for Vehicle Positioning. In: IEEE 69th Vehicular
Technology Conference (VTC 2009 Spring). ; 2009:1-5. doi:10.1109/VETECS.2009.5073634'
apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.
In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring) (pp. 1–5).
https://doi.org/10.1109/VETECS.2009.5073634
bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning},
DOI={10.1109/VETECS.2009.5073634},
booktitle={IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)}, author={Bevermeier,
Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009}, pages={1–5} }'
chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Joint Parameter
Estimation and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning.”
In IEEE 69th Vehicular Technology Conference (VTC 2009 Spring), 1–5, 2009.
https://doi.org/10.1109/VETECS.2009.5073634.
ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Joint Parameter Estimation
and Tracking in a Multi-Stage Kalman Filter for Vehicle Positioning,” in IEEE
69th Vehicular Technology Conference (VTC 2009 Spring), 2009, pp. 1–5.
mla: Bevermeier, Maik, et al. “Joint Parameter Estimation and Tracking in a Multi-Stage
Kalman Filter for Vehicle Positioning.” IEEE 69th Vehicular Technology Conference
(VTC 2009 Spring), 2009, pp. 1–5, doi:10.1109/VETECS.2009.5073634.
short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: IEEE 69th Vehicular Technology
Conference (VTC 2009 Spring), 2009, pp. 1–5.'
date_created: 2019-07-12T05:27:02Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/VETECS.2009.5073634
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09-1.pdf
oa: '1'
page: 1-5
publication: IEEE 69th Vehicular Technology Conference (VTC 2009 Spring)
status: public
title: Joint Parameter Estimation and Tracking in a Multi-Stage Kalman Filter for
Vehicle Positioning
type: conference
user_id: '44006'
year: '2009'
...
---
_id: '11938'
abstract:
- lang: eng
text: 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:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Windmann S, Haeb-Umbach R. Parameter Estimation of a State-Space Model of Noise
for Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language
Processing. 2009;17(8):1577-1590. doi:10.1109/TASL.2009.2023172
apa: Windmann, S., & Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition. IEEE Transactions on Audio, Speech,
and Language Processing, 17(8), 1577–1590. https://doi.org/10.1109/TASL.2009.2023172
bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition}, volume={17}, DOI={10.1109/TASL.2009.2023172},
number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590}
}'
chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a
State-Space Model of Noise for Robust Speech Recognition.” IEEE Transactions
on Audio, Speech, and Language Processing 17, no. 8 (2009): 1577–90. https://doi.org/10.1109/TASL.2009.2023172.'
ieee: S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model
of Noise for Robust Speech Recognition,” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 17, no. 8, pp. 1577–1590, 2009.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition.” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 17, no. 8, 2009, pp. 1577–90, doi:10.1109/TASL.2009.2023172.
short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 17 (2009) 1577–1590.
date_created: 2019-07-12T05:31:09Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2023172
intvolume: ' 17'
issue: '8'
keyword:
- 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
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf
oa: '1'
page: 1577-1590
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11927'
abstract:
- lang: eng
text: Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the
presence of spatially colored noise leads to a generalized eigenvalue problem.
While this approach has extensively been employed in narrowband (antenna) array
beamforming, it is typically not used for broadband (microphone) array beamforming
due to the uncontrolled amount of speech distortion introduced by a narrowband
SNR criterion. In this paper, we show how the distortion of the desired signal
can be controlled by a single-channel post-filter, resulting in a performance
comparable to the generalized minimum variance distortionless response beamformer,
where arbitrary transfer functions relate the source and the microphones. Results
are given both for directional and diffuse noise. A novel gradient ascent adaptation
algorithm is presented, and its good convergence properties are experimentally
revealed by comparison with alternatives from the literature. A key feature of
the proposed beamformer is that it operates blindly, i.e., it neither requires
knowledge about the array geometry nor an explicit estimation of the transfer
functions from source to sensors or the direction-of-arrival.
author:
- 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: Warsitz E, Haeb-Umbach R. Blind Acoustic Beamforming Based on Generalized Eigenvalue
Decomposition. IEEE Transactions on Audio, Speech, and Language Processing.
2007;15(5):1529-1539. doi:10.1109/TASL.2007.898454
apa: Warsitz, E., & Haeb-Umbach, R. (2007). Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition. IEEE Transactions on Audio, Speech,
and Language Processing, 15(5), 1529–1539. https://doi.org/10.1109/TASL.2007.898454
bibtex: '@article{Warsitz_Haeb-Umbach_2007, title={Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition}, volume={15}, DOI={10.1109/TASL.2007.898454},
number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2007}, pages={1529–1539}
}'
chicago: 'Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming
Based on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio,
Speech, and Language Processing 15, no. 5 (2007): 1529–39. https://doi.org/10.1109/TASL.2007.898454.'
ieee: E. Warsitz and R. Haeb-Umbach, “Blind Acoustic Beamforming Based on Generalized
Eigenvalue Decomposition,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 15, no. 5, pp. 1529–1539, 2007.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 15, no. 5, 2007, pp. 1529–39, doi:10.1109/TASL.2007.898454.
short: E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 15 (2007) 1529–1539.
date_created: 2019-07-12T05:30:57Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2007.898454
intvolume: ' 15'
issue: '5'
keyword:
- acoustic signal processing
- arbitrary transfer function
- array signal processing
- blind acoustic beamforming
- direction-of-arrival
- direction-of-arrival estimation
- eigenvalues and eigenfunctions
- generalized eigenvalue decomposition
- gradient ascent adaptation algorithm
- microphone arrays
- microphones
- narrowband array beamforming
- sensor array
- single-channel post-filter
- spatially colored noise
- transfer functions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/WaHa07.pdf
oa: '1'
page: 1529-1539
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition
type: journal_article
user_id: '44006'
volume: 15
year: '2007'
...
---
_id: '11943'
abstract:
- lang: eng
text: A marginalized particle filter is proposed for performing single channel speech
enhancement with a non-linear dynamic state model. The system consists of a particle
filter for tracking line spectral pair (LSP) parameters and a Kalman filter per
particle for speech enhancement. The state model for the LSPs has been learnt
on clean speech training data. In our approach parameters and speech samples are
processed at different time scales by assuming the parameters to be constant for
small blocks of data. Further enhancement is obtained by an iteration which can
be applied on these small blocks. The experiments show that similar SNR gains
are obtained as with the Kalman-LM-iterative algorithm. However better values
of the noise level and the log-spectral distance are achieved
author:
- first_name: Stefan
full_name: Windmann, Stefan
last_name: Windmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006). Vol 1. ; 2006:I.
doi:10.1109/ICASSP.2006.1660058'
apa: Windmann, S., & Haeb-Umbach, R. (2006). Iterative Speech Enhancement using
a Non-Linear Dynamic State Model of Speech and its Parameters. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) (Vol.
1, p. I). https://doi.org/10.1109/ICASSP.2006.1660058
bibtex: '@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement
using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1},
DOI={10.1109/ICASSP.2006.1660058},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006},
pages={I} }'
chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement
Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006),
1:I, 2006. https://doi.org/10.1109/ICASSP.2006.1660058.
ieee: S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006), 2006, vol. 1, p.
I.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using
a Non-Linear Dynamic State Model of Speech and Its Parameters.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol.
1, 2006, p. I, doi:10.1109/ICASSP.2006.1660058.
short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:31:15Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1660058
intvolume: ' 1'
keyword:
- clean speech training data
- iterative methods
- iterative speech enhancement
- Kalman filter
- Kalman filters
- Kalman-LM-iterative algorithm
- line spectral pair parameters
- log-spectral distance
- marginalized particle filter
- noise level
- nonlinear dynamic state speech model
- particle filtering (numerical methods)
- single channel speech enhancement
- SNR gains
- speech enhancement
- speech samples
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2006)
status: public
title: Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech
and its Parameters
type: conference
user_id: '44006'
volume: 1
year: '2006'
...
---
_id: '11930'
abstract:
- lang: eng
text: For human-machine interfaces in distant-talking environments multichannel
signal processing is often employed to obtain an enhanced signal for subsequent
processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum
beamformer to adjust the coefficients of FIR filters to changing acoustic room
impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient
ascent algorithm are derived from a constrained optimization problem, which iteratively
estimates the eigenvector corresponding to the largest eigenvalue of the cross
power spectral density of the microphone signals. The method does not require
an explicit estimation of the speaker location. The experimental results show
fast adaptation and excellent robustness of the proposed algorithm.
author:
- 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: 'Warsitz E, Haeb-Umbach R. Acoustic filter-and-sum beamforming by adaptive
principal component analysis. In: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005). Vol 4. ; 2005:iv/797-iv/800 Vol.
4. doi:10.1109/ICASSP.2005.1416129'
apa: Warsitz, E., & Haeb-Umbach, R. (2005). Acoustic filter-and-sum beamforming
by adaptive principal component analysis. In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2005) (Vol. 4, p. iv/797-iv/800
Vol. 4). https://doi.org/10.1109/ICASSP.2005.1416129
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2005, title={Acoustic filter-and-sum
beamforming by adaptive principal component analysis}, volume={4}, DOI={10.1109/ICASSP.2005.1416129},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2005},
pages={iv/797-iv/800 Vol. 4} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
by Adaptive Principal Component Analysis.” In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2005), 4:iv/797-iv/800
Vol. 4, 2005. https://doi.org/10.1109/ICASSP.2005.1416129.
ieee: E. Warsitz and R. Haeb-Umbach, “Acoustic filter-and-sum beamforming by adaptive
principal component analysis,” in IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005), 2005, vol. 4, p. iv/797-iv/800
Vol. 4.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
by Adaptive Principal Component Analysis.” IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2005), vol. 4, 2005, p. iv/797-iv/800
Vol. 4, doi:10.1109/ICASSP.2005.1416129.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2005), 2005, p. iv/797-iv/800 Vol. 4.'
date_created: 2019-07-12T05:31:00Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2005.1416129
intvolume: ' 4'
keyword:
- acoustic filter-and-sum beamforming
- acoustic room impulses
- acoustic signal processing
- adaptive principal component analysis
- adaptive signal processing
- architectural acoustics
- constrained optimization problem
- cross power spectral density
- deterministic algorithm
- deterministic algorithms
- distant-talking environments
- eigenvalues and eigenfunctions
- eigenvector
- enhanced signal
- filter-and-sum beamformer
- FIR filter coefficients
- FIR filter coefficients
- FIR filters
- gradient methods
- human-machine interfaces
- iterative estimation
- iterative methods
- largest eigenvalue
- microphone signals
- multichannel signal processing
- optimisation
- principal component analysis
- spectral analysis
- stochastic gradient ascent algorithm
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2005/WaHa05.pdf
oa: '1'
page: iv/797-iv/800 Vol. 4
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2005)
status: public
title: Acoustic filter-and-sum beamforming by adaptive principal component analysis
type: conference
user_id: '44006'
volume: 4
year: '2005'
...
---
_id: '11931'
abstract:
- lang: eng
text: The paper is concerned with binaural signal processing for a bimodal human-robot
interface with hearing and vision. The two microphone signals are processed to
obtain an enhanced single-channel input signal for the subsequent speech recognizer
and to localize the acoustic source, an important information for establishing
a natural human-robot communication. We utilize a robust adaptive algorithm for
filter-and-sum beamforming (FSB) and extract speaker direction information from
the resulting FIR filter coefficients. Further, particle filtering is applied
which conducts a nonlinear Bayesian tracking of speaker movement. Good location
accuracy can be achieved even in highly reverberant environments. The results
obtained outperform the conventional generalized cross correlation (GCC) method.
author:
- 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: 'Warsitz E, Haeb-Umbach R. Robust speaker direction estimation with particle
filtering. In: IEEE Workshop on Multimedia Signal Processing (MMSP 2004).
; 2004:367-370. doi:10.1109/MMSP.2004.1436569'
apa: Warsitz, E., & Haeb-Umbach, R. (2004). Robust speaker direction estimation
with particle filtering. In IEEE Workshop on Multimedia Signal Processing (MMSP
2004) (pp. 367–370). https://doi.org/10.1109/MMSP.2004.1436569
bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2004, title={Robust speaker direction
estimation with particle filtering}, DOI={10.1109/MMSP.2004.1436569},
booktitle={IEEE Workshop on Multimedia Signal Processing (MMSP 2004)}, author={Warsitz,
Ernst and Haeb-Umbach, Reinhold}, year={2004}, pages={367–370} }'
chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” In IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 367–70, 2004. https://doi.org/10.1109/MMSP.2004.1436569.
ieee: E. Warsitz and R. Haeb-Umbach, “Robust speaker direction estimation with particle
filtering,” in IEEE Workshop on Multimedia Signal Processing (MMSP 2004),
2004, pp. 367–370.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Robust Speaker Direction Estimation
with Particle Filtering.” IEEE Workshop on Multimedia Signal Processing (MMSP
2004), 2004, pp. 367–70, doi:10.1109/MMSP.2004.1436569.
short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE Workshop on Multimedia Signal Processing
(MMSP 2004), 2004, pp. 367–370.'
date_created: 2019-07-12T05:31:01Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/MMSP.2004.1436569
keyword:
- bimodal human-robot interface
- binaural signal processing
- enhanced single-channel input signal
- filter-and-sum beamforming
- filtering theory
- FIR filter coefficient
- generalized cross correlation method
- microphones
- microphone signal
- nonlinear Bayesian tracking
- particle filtering
- robust adaptive algorithm
- robust speaker direction estimation
- signal processing
- speech enhancement
- speech recognition
- speech recognizer
- user interfaces
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2004/WaHa04.pdf
oa: '1'
page: 367-370
publication: IEEE Workshop on Multimedia Signal Processing (MMSP 2004)
status: public
title: Robust speaker direction estimation with particle filtering
type: conference
user_id: '44006'
year: '2004'
...