---
_id: '12899'
abstract:
- lang: eng
text: This contribution presents a speech enhancement system for the CHiME-5 Dinner
Party Scenario. The front-end employs multi-channel linear time-variant filtering
and achieves its gains without the use of a neural network. We present an adaptation
of blind source separation techniques to the CHiME-5 database which we call Guided
Source Separation (GSS). Using the baseline acoustic and language model, the combination
of Weighted Prediction Error based dereverberation, guided source separation,
and beamforming reduces the WER by 10:54% (relative) for the single array track
and by 21:12% (relative) on the multiple array track.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Heitkaemper J, Schmalenstroeer J, Drude L, Heymann J, Haeb-Umbach
R. Front-End Processing for the CHiME-5 Dinner Party Scenario. In: Proc. CHiME
2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India.
; 2018.'
apa: Boeddeker, C., Heitkaemper, J., Schmalenstroeer, J., Drude, L., Heymann, J.,
& Haeb-Umbach, R. (2018). Front-End Processing for the CHiME-5 Dinner Party
Scenario. Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India.
bibtex: '@inproceedings{Boeddeker_Heitkaemper_Schmalenstroeer_Drude_Heymann_Haeb-Umbach_2018,
title={Front-End Processing for the CHiME-5 Dinner Party Scenario}, booktitle={Proc.
CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
India}, author={Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer,
Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2018}
}'
chicago: Boeddeker, Christoph, Jens Heitkaemper, Joerg Schmalenstroeer, Lukas Drude,
Jahn Heymann, and Reinhold Haeb-Umbach. “Front-End Processing for the CHiME-5
Dinner Party Scenario.” In Proc. CHiME 2018 Workshop on Speech Processing in
Everyday Environments, Hyderabad, India, 2018.
ieee: C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, and
R. Haeb-Umbach, “Front-End Processing for the CHiME-5 Dinner Party Scenario,”
2018.
mla: Boeddeker, Christoph, et al. “Front-End Processing for the CHiME-5 Dinner Party
Scenario.” Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India, 2018.
short: 'C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann,
R. Haeb-Umbach, in: Proc. CHiME 2018 Workshop on Speech Processing in Everyday
Environments, Hyderabad, India, 2018.'
date_created: 2019-07-30T14:35:15Z
date_updated: 2023-10-26T08:14:15Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Paper.pdf
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Poster.pdf
status: public
title: Front-End Processing for the CHiME-5 Dinner Party Scenario
type: conference
user_id: '460'
year: '2018'
...
---
_id: '6859'
abstract:
- lang: eng
text: "Signal processing in WASNs is based on a software framework for hosting the
algorithms as well as on a set of wireless connected devices representing the
hardware. Each of the nodes contributes memory, processing power, communication
bandwidth and some sensor information for the tasks to be solved on the network.
\r\nIn this paper we present our MARVELO framework for distributed signal processing.
It is intended for transforming existing centralized implementations into distributed
versions. To this end, the software only needs a block-oriented implementation,
which MARVELO picks-up and distributes on the network. Additionally, our sensor
node hardware and the audio interfaces responsible for multi-channel recordings
are presented."
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Joerg
full_name: Ullmann, Joerg
id: '16256'
last_name: Ullmann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Schmalenstroeer J, Ullmann J, Haeb-Umbach R, Karl H. MARVELO - A
Framework for Signal Processing in Wireless Acoustic Sensor Networks. In: Speech
Communication; 13th ITG-Symposium. ; 2018:1-5.'
apa: Afifi, H., Schmalenstroeer, J., Ullmann, J., Haeb-Umbach, R., & Karl, H.
(2018). MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor
Networks. Speech Communication; 13th ITG-Symposium, 1–5.
bibtex: '@inproceedings{Afifi_Schmalenstroeer_Ullmann_Haeb-Umbach_Karl_2018, title={MARVELO
- A Framework for Signal Processing in Wireless Acoustic Sensor Networks}, booktitle={Speech
Communication; 13th ITG-Symposium}, author={Afifi, Haitham and Schmalenstroeer,
Joerg and Ullmann, Joerg and Haeb-Umbach, Reinhold and Karl, Holger}, year={2018},
pages={1–5} }'
chicago: Afifi, Haitham, Joerg Schmalenstroeer, Joerg Ullmann, Reinhold Haeb-Umbach,
and Holger Karl. “MARVELO - A Framework for Signal Processing in Wireless Acoustic
Sensor Networks.” In Speech Communication; 13th ITG-Symposium, 1–5, 2018.
ieee: H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, and H. Karl, “MARVELO
- A Framework for Signal Processing in Wireless Acoustic Sensor Networks,” in
Speech Communication; 13th ITG-Symposium, 2018, pp. 1–5.
mla: Afifi, Haitham, et al. “MARVELO - A Framework for Signal Processing in Wireless
Acoustic Sensor Networks.” Speech Communication; 13th ITG-Symposium, 2018,
pp. 1–5.
short: 'H. Afifi, J. Schmalenstroeer, J. Ullmann, R. Haeb-Umbach, H. Karl, in: Speech
Communication; 13th ITG-Symposium, 2018, pp. 1–5.'
date_created: 2019-01-17T15:47:35Z
date_updated: 2023-10-26T08:15:32Z
department:
- _id: '75'
- _id: '54'
language:
- iso: eng
page: 1-5
project:
- _id: '27'
name: 'Akustische Sensornetzwerke - Teilprojekt '
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: Speech Communication; 13th ITG-Symposium
quality_controlled: '1'
status: public
title: MARVELO - A Framework for Signal Processing in Wireless Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11747'
abstract:
- lang: eng
text: In this paper, we present a neural network based classification algorithm
for the discrimination of moving from stationary targets in the sight of an automotive
radar sensor. Compared to existing algorithms, the proposed algorithm can take
into account multiple local radar targets instead of performing classification
inference on each target individually resulting in superior discrimination accuracy,
especially suitable for non rigid objects, like pedestrians, which in general
have a wide velocity spread when multiple targets are detected.
author:
- first_name: Christopher
full_name: Grimm, Christopher
last_name: Grimm
- first_name: Tobias
full_name: Breddermann, Tobias
last_name: Breddermann
- first_name: Ridha
full_name: Farhoud, Ridha
last_name: Farhoud
- first_name: Tai
full_name: Fei, Tai
last_name: Fei
- 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: 'Grimm C, Breddermann T, Farhoud R, Fei T, Warsitz E, Haeb-Umbach R. Discrimination
of Stationary from Moving Targets with Recurrent Neural Networks in Automotive
Radar. In: International Conference on Microwaves for Intelligent Mobility
(ICMIM) 2018. ; 2018.'
apa: Grimm, C., Breddermann, T., Farhoud, R., Fei, T., Warsitz, E., & Haeb-Umbach,
R. (2018). Discrimination of Stationary from Moving Targets with Recurrent Neural
Networks in Automotive Radar. International Conference on Microwaves for Intelligent
Mobility (ICMIM) 2018.
bibtex: '@inproceedings{Grimm_Breddermann_Farhoud_Fei_Warsitz_Haeb-Umbach_2018,
title={Discrimination of Stationary from Moving Targets with Recurrent Neural
Networks in Automotive Radar}, booktitle={International Conference on Microwaves
for Intelligent Mobility (ICMIM) 2018}, author={Grimm, Christopher and Breddermann,
Tobias and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach, Reinhold},
year={2018} }'
chicago: Grimm, Christopher, Tobias Breddermann, Ridha Farhoud, Tai Fei, Ernst Warsitz,
and Reinhold Haeb-Umbach. “Discrimination of Stationary from Moving Targets with
Recurrent Neural Networks in Automotive Radar.” In International Conference
on Microwaves for Intelligent Mobility (ICMIM) 2018, 2018.
ieee: C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach,
“Discrimination of Stationary from Moving Targets with Recurrent Neural Networks
in Automotive Radar,” 2018.
mla: Grimm, Christopher, et al. “Discrimination of Stationary from Moving Targets
with Recurrent Neural Networks in Automotive Radar.” International Conference
on Microwaves for Intelligent Mobility (ICMIM) 2018, 2018.
short: 'C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach,
in: International Conference on Microwaves for Intelligent Mobility (ICMIM) 2018,
2018.'
date_created: 2019-07-12T05:27:29Z
date_updated: 2023-11-20T16:37:39Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICMIM_2018_Haeb-Umbach_Paper.pdf
oa: '1'
publication: International Conference on Microwaves for Intelligent Mobility (ICMIM)
2018
quality_controlled: '1'
status: public
title: Discrimination of Stationary from Moving Targets with Recurrent Neural Networks
in Automotive Radar
type: conference
user_id: '242'
year: '2018'
...
---
_id: '11907'
abstract:
- lang: eng
text: The invention of the Variational Autoencoder enables the application of Neural
Networks to a wide range of tasks in unsupervised learning, including the field
of Acoustic Unit Discovery (AUD). The recently proposed Hidden Markov Model Variational
Autoencoder (HMMVAE) allows a joint training of a neural network based feature
extractor and a structured prior for the latent space given by a Hidden Markov
Model. It has been shown that the HMMVAE significantly outperforms pure GMM-HMM
based systems on the AUD task. However, the HMMVAE cannot autonomously infer the
number of acoustic units and thus relies on the GMM-HMM system for initialization.
This paper introduces the Bayesian Hidden Markov Model Variational Autoencoder
(BHMMVAE) which solves these issues by embedding the HMMVAE in a Bayesian framework
with a Dirichlet Process Prior for the distribution of the acoustic units, and
diagonal or full-covariance Gaussians as emission distributions. Experiments on
TIMIT and Xitsonga show that the BHMMVAE is able to autonomously infer a reasonable
number of acoustic units, can be initialized without supervision by a GMM-HMM
system, achieves computationally efficient stochastic variational inference by
using natural gradient descent, and, additionally, improves the AUD performance
over the HMMVAE.
author:
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Glarner T, Hanebrink P, Ebbers J, Haeb-Umbach R. Full Bayesian Hidden Markov
Model Variational Autoencoder for Acoustic Unit Discovery. In: INTERSPEECH
2018, Hyderabad, India. ; 2018.'
apa: Glarner, T., Hanebrink, P., Ebbers, J., & Haeb-Umbach, R. (2018). Full
Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.
INTERSPEECH 2018, Hyderabad, India.
bibtex: '@inproceedings{Glarner_Hanebrink_Ebbers_Haeb-Umbach_2018, title={Full Bayesian
Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH
2018, Hyderabad, India}, author={Glarner, Thomas and Hanebrink, Patrick and Ebbers,
Janek and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Glarner, Thomas, Patrick Hanebrink, Janek Ebbers, and Reinhold Haeb-Umbach.
“Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.”
In INTERSPEECH 2018, Hyderabad, India, 2018.
ieee: T. Glarner, P. Hanebrink, J. Ebbers, and R. Haeb-Umbach, “Full Bayesian Hidden
Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2018.
mla: Glarner, Thomas, et al. “Full Bayesian Hidden Markov Model Variational Autoencoder
for Acoustic Unit Discovery.” INTERSPEECH 2018, Hyderabad, India, 2018.
short: 'T. Glarner, P. Hanebrink, J. Ebbers, R. Haeb-Umbach, in: INTERSPEECH 2018,
Hyderabad, India, 2018.'
date_created: 2019-07-12T05:30:34Z
date_updated: 2023-11-22T08:29:22Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Paper.pdf
oa: '1'
publication: INTERSPEECH 2018, Hyderabad, India
quality_controlled: '1'
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Glarner_Slides.pdf
status: public
title: Full Bayesian Hidden Markov Model Variational Autoencoder for Acoustic Unit
Discovery
type: conference
user_id: '34851'
year: '2018'
...
---
_id: '46350'
abstract:
- lang: eng
text: 'The ubiquity of WiFi access points and the sharp increase in WiFi-enabled
devices carried by humans have paved the way for WiFi-based indoor positioning
and location analysis. Locating people in indoor environments has numerous applications
in robotics, crowd control, indoor facility optimization, and automated environment
mapping. However, existing WiFi-based positioning systems suffer from two major
problems: (1) their accuracy and precision is limited due to inherent noise induced
by indoor obstacles, and (2) they only occasionally provide location estimates,
namely when a WiFi-equipped device emits a signal. To mitigate these two issues,
we propose a novel Gaussian process (GP) model for WiFi signal strength measurements.
It allows for simultaneous smoothing (increasing accuracy and precision of estimators)
and interpolation (enabling continuous sampling of location estimates). Furthermore,
simple and efficient smoothing methods for location estimates are introduced to
improve localization performance in real-time settings. Experiments are conducted
on two data sets from a large real-world commercial indoor retail environment.
Results demonstrate that our approach provides significant improvements in terms
of precision and accuracy with respect to unfiltered data. Ultimately, the GP
model realizes continuous location sampling with consistently high quality location
estimates.'
author:
- first_name: J.E.
full_name: van Engelen, J.E.
last_name: van Engelen
- first_name: J.J.
full_name: van Lier, J.J.
last_name: van Lier
- first_name: F.W.
full_name: Takes, F.W.
last_name: Takes
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: 'van Engelen JE, van Lier JJ, Takes FW, Trautmann H. Accurate WiFi based indoor
positioning with continuous location sampling. In: Proceedings of the European
Conference on Machine Learning and Principles and Practice of Knowledge Discovery
in Database (ECML/PKDD). Springer; 2018:524–540.'
apa: van Engelen, J. E., van Lier, J. J., Takes, F. W., & Trautmann, H. (2018).
Accurate WiFi based indoor positioning with continuous location sampling. Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 524–540.
bibtex: '@inproceedings{van Engelen_van Lier_Takes_Trautmann_2018, place={Dublin,
Ireland}, title={Accurate WiFi based indoor positioning with continuous location
sampling}, booktitle={Proceedings of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD)}, publisher={Springer},
author={van Engelen, J.E. and van Lier, J.J. and Takes, F.W. and Trautmann, Heike},
year={2018}, pages={524–540} }'
chicago: 'Engelen, J.E. van, J.J. van Lier, F.W. Takes, and Heike Trautmann. “Accurate
WiFi Based Indoor Positioning with Continuous Location Sampling.” In Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 524–540. Dublin, Ireland: Springer,
2018.'
ieee: J. E. van Engelen, J. J. van Lier, F. W. Takes, and H. Trautmann, “Accurate
WiFi based indoor positioning with continuous location sampling,” in Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), 2018, pp. 524–540.
mla: van Engelen, J. E., et al. “Accurate WiFi Based Indoor Positioning with Continuous
Location Sampling.” Proceedings of the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery in Database (ECML/PKDD),
Springer, 2018, pp. 524–540.
short: 'J.E. van Engelen, J.J. van Lier, F.W. Takes, H. Trautmann, in: Proceedings
of the European Conference on Machine Learning and Principles and Practice of
Knowledge Discovery in Database (ECML/PKDD), Springer, Dublin, Ireland, 2018,
pp. 524–540.'
date_created: 2023-08-04T07:54:43Z
date_updated: 2023-10-16T13:33:18Z
department:
- _id: '34'
- _id: '819'
language:
- iso: eng
page: 524–540
place: Dublin, Ireland
publication: Proceedings of the European Conference on Machine Learning and Principles
and Practice of Knowledge Discovery in Database (ECML/PKDD)
publisher: Springer
status: public
title: Accurate WiFi based indoor positioning with continuous location sampling
type: conference
user_id: '15504'
year: '2018'
...
---
_id: '46351'
abstract:
- lang: eng
text: Clustering is an important field in data mining that aims to reveal hidden
patterns in data sets. It is widely popular in marketing or medical applications
and used to identify groups of similar objects. Clustering possibly unbounded
and evolving data streams is of particular interest due to the widespread deployment
of large and fast data sources such as sensors. The vast majority of stream clustering
algorithms employ a two-phase approach where the stream is first summarized in
an online phase. Upon request, an offline phase reclusters the aggregations into
the final clusters. In this setup, the online component will idle and wait for
the next observation in times where the stream is slow. This paper proposes a
new stream clustering algorithm called evoStream which performs evolutionary optimization
in the idle times of the online phase to incrementally build and refine the final
clusters. Since the online phase would idle otherwise, our approach does not reduce
the processing speed while effectively removing the computational overhead of
the offline phase. In extensive experiments on real data streams we show that
the proposed algorithm allows to output clusters of high quality at any time within
the stream without the need for additional computational resources.
author:
- first_name: Matthias
full_name: Carnein, Matthias
last_name: Carnein
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
citation:
ama: Carnein M, Trautmann H. evoStream — Evolutionary Stream Clustering Utilizing
Idle Times. Big Data Research. 2018;14:101–111. doi:10.1016/j.bdr.2018.05.005
apa: Carnein, M., & Trautmann, H. (2018). evoStream — Evolutionary Stream Clustering
Utilizing Idle Times. Big Data Research, 14, 101–111. https://doi.org/10.1016/j.bdr.2018.05.005
bibtex: '@article{Carnein_Trautmann_2018, title={evoStream — Evolutionary Stream
Clustering Utilizing Idle Times}, volume={14}, DOI={10.1016/j.bdr.2018.05.005},
journal={Big Data Research}, author={Carnein, Matthias and Trautmann, Heike},
year={2018}, pages={101–111} }'
chicago: 'Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream
Clustering Utilizing Idle Times.” Big Data Research 14 (2018): 101–111.
https://doi.org/10.1016/j.bdr.2018.05.005.'
ieee: 'M. Carnein and H. Trautmann, “evoStream — Evolutionary Stream Clustering
Utilizing Idle Times,” Big Data Research, vol. 14, pp. 101–111, 2018, doi:
10.1016/j.bdr.2018.05.005.'
mla: Carnein, Matthias, and Heike Trautmann. “EvoStream — Evolutionary Stream Clustering
Utilizing Idle Times.” Big Data Research, vol. 14, 2018, pp. 101–111, doi:10.1016/j.bdr.2018.05.005.
short: M. Carnein, H. Trautmann, Big Data Research 14 (2018) 101–111.
date_created: 2023-08-04T07:55:33Z
date_updated: 2023-10-16T13:33:43Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.bdr.2018.05.005
intvolume: ' 14'
language:
- iso: eng
page: 101–111
publication: Big Data Research
status: public
title: evoStream — Evolutionary Stream Clustering Utilizing Idle Times
type: journal_article
user_id: '15504'
volume: 14
year: '2018'
...
---
_id: '46353'
abstract:
- lang: eng
text: 'Incorporating decision makers'' preferences is of great significance in multiobjective
optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs),
aiming at a well-distributed subset of Pareto optimal solutions within the user-provided
region(s), are extensively investigated in this paper. An empirical comparison
is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA.
Experimental results show that T-SMS-EMOA has the best overall performance regarding
the hypervolume indicator within the target region, while T-NSGA-II is the fastest
algorithm. We also compare TMOEAs with other state-of-the-art preference-based
approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages
of TMOEAs. A case study in the mission planning of earth observation satellite
is carried out to verify the capabilities of TMOEAs in the real-world application.
Experimental results indicate that preferences can improve the searching ability
of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by
the decision maker.'
author:
- first_name: L
full_name: Li, L
last_name: Li
- first_name: Y
full_name: Wang, Y
last_name: Wang
- first_name: Heike
full_name: Trautmann, Heike
id: '100740'
last_name: Trautmann
orcid: 0000-0002-9788-8282
- first_name: N
full_name: Jing, N
last_name: Jing
- first_name: M
full_name: Emmerich, M
last_name: Emmerich
citation:
ama: Li L, Wang Y, Trautmann H, Jing N, Emmerich M. Multiobjective evolutionary
algorithms based on target region preferences. Swarm and Evolutionary Computation.
2018;40:196–215. doi:10.1016/j.swevo.2018.02.006
apa: Li, L., Wang, Y., Trautmann, H., Jing, N., & Emmerich, M. (2018). Multiobjective
evolutionary algorithms based on target region preferences. Swarm and Evolutionary
Computation, 40, 196–215. https://doi.org/10.1016/j.swevo.2018.02.006
bibtex: '@article{Li_Wang_Trautmann_Jing_Emmerich_2018, title={Multiobjective evolutionary
algorithms based on target region preferences}, volume={40}, DOI={10.1016/j.swevo.2018.02.006},
journal={Swarm and Evolutionary Computation}, author={Li, L and Wang, Y and Trautmann,
Heike and Jing, N and Emmerich, M}, year={2018}, pages={196–215} }'
chicago: 'Li, L, Y Wang, Heike Trautmann, N Jing, and M Emmerich. “Multiobjective
Evolutionary Algorithms Based on Target Region Preferences.” Swarm and Evolutionary
Computation 40 (2018): 196–215. https://doi.org/10.1016/j.swevo.2018.02.006.'
ieee: 'L. Li, Y. Wang, H. Trautmann, N. Jing, and M. Emmerich, “Multiobjective evolutionary
algorithms based on target region preferences,” Swarm and Evolutionary Computation,
vol. 40, pp. 196–215, 2018, doi: 10.1016/j.swevo.2018.02.006.'
mla: Li, L., et al. “Multiobjective Evolutionary Algorithms Based on Target Region
Preferences.” Swarm and Evolutionary Computation, vol. 40, 2018, pp. 196–215,
doi:10.1016/j.swevo.2018.02.006.
short: L. Li, Y. Wang, H. Trautmann, N. Jing, M. Emmerich, Swarm and Evolutionary
Computation 40 (2018) 196–215.
date_created: 2023-08-04T07:56:57Z
date_updated: 2023-10-16T13:34:21Z
department:
- _id: '34'
- _id: '819'
doi: 10.1016/j.swevo.2018.02.006
intvolume: ' 40'
language:
- iso: eng
page: 196–215
publication: Swarm and Evolutionary Computation
status: public
title: Multiobjective evolutionary algorithms based on target region preferences
type: journal_article
user_id: '15504'
volume: 40
year: '2018'
...
---
_id: '11838'
abstract:
- lang: eng
text: Distributed sensor data acquisition usually encompasses data sampling by the
individual devices, where each of them has its own oscillator driving the local
sampling process, resulting in slightly different sampling rates at the individual
sensor nodes. Nevertheless, for certain downstream signal processing tasks it
is important to compensate even for small sampling rate offsets. Aligning the
sampling rates of oscillators which differ only by a few parts-per-million, is,
however, challenging and quite different from traditional multirate signal processing
tasks. In this paper we propose to transfer a precise but computationally demanding
time domain approach, inspired by the Nyquist-Shannon sampling theorem, to an
efficient frequency domain implementation. To this end a buffer control is employed
which compensates for sampling offsets which are multiples of the sampling period,
while a digital filter, realized by the wellknown Overlap-Save method, handles
the fractional part of the sampling phase offset. With experiments on artificially
misaligned data we investigate the parametrization, the efficiency, and the induced
distortions of the proposed resampling method. It is shown that a favorable compromise
between residual distortion and computational complexity is achieved, compared
to other sampling rate offset compensation techniques.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Haeb-Umbach R. Efficient Sampling Rate Offset Compensation
- An Overlap-Save Based Approach. In: 26th European Signal Processing Conference
(EUSIPCO 2018). ; 2018.'
apa: Schmalenstroeer, J., & Haeb-Umbach, R. (2018). Efficient Sampling Rate
Offset Compensation - An Overlap-Save Based Approach. 26th European Signal
Processing Conference (EUSIPCO 2018).
bibtex: '@inproceedings{Schmalenstroeer_Haeb-Umbach_2018, title={Efficient Sampling
Rate Offset Compensation - An Overlap-Save Based Approach}, booktitle={26th European
Signal Processing Conference (EUSIPCO 2018)}, author={Schmalenstroeer, Joerg and
Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Efficient Sampling Rate
Offset Compensation - An Overlap-Save Based Approach.” In 26th European Signal
Processing Conference (EUSIPCO 2018), 2018.
ieee: J. Schmalenstroeer and R. Haeb-Umbach, “Efficient Sampling Rate Offset Compensation
- An Overlap-Save Based Approach,” 2018.
mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Efficient Sampling Rate
Offset Compensation - An Overlap-Save Based Approach.” 26th European Signal
Processing Conference (EUSIPCO 2018), 2018.
short: 'J. Schmalenstroeer, R. Haeb-Umbach, in: 26th European Signal Processing
Conference (EUSIPCO 2018), 2018.'
date_created: 2019-07-12T05:29:14Z
date_updated: 2023-10-26T08:12:33Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/Eusipco_2018_Schmalenstroeer_Paper.pdf
oa: '1'
publication: 26th European Signal Processing Conference (EUSIPCO 2018)
quality_controlled: '1'
status: public
title: Efficient Sampling Rate Offset Compensation - An Overlap-Save Based Approach
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11876'
abstract:
- lang: eng
text: This paper describes the systems for the single-array track and the multiple-array
track of the 5th CHiME Challenge. The final system is a combination of multiple
systems, using Confusion Network Combination (CNC). The different systems presented
here are utilizing different front-ends and training sets for a Bidirectional
Long Short-Term Memory (BLSTM) Acoustic Model (AM). The front-end was replaced
by enhancements provided by Paderborn University [1]. The back-end has been implemented
using RASR [2] and RETURNN [3]. Additionally, a system combination including the
hypothesis word graphs from the system of the submission [1] has been performed,
which results in the final best system.
author:
- first_name: Markus
full_name: Kitza, Markus
last_name: Kitza
- first_name: Wilfried
full_name: Michel, Wilfried
last_name: Michel
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Tobias
full_name: Menne, Tobias
last_name: Menne
- first_name: Ralf
full_name: Schlüter, Ralf
last_name: Schlüter
- first_name: Hermann
full_name: Ney, Hermann
last_name: Ney
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Kitza M, Michel W, Boeddeker C, et al. The RWTH/UPB System Combination for
the CHiME 2018 Workshop. In: Proc. CHiME 2018 Workshop on Speech Processing
in Everyday Environments, Hyderabad, India. ; 2018.'
apa: Kitza, M., Michel, W., Boeddeker, C., Heitkaemper, J., Menne, T., Schlüter,
R., Ney, H., Schmalenstroeer, J., Drude, L., Heymann, J., & Haeb-Umbach, R.
(2018). The RWTH/UPB System Combination for the CHiME 2018 Workshop. Proc.
CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
India.
bibtex: '@inproceedings{Kitza_Michel_Boeddeker_Heitkaemper_Menne_Schlüter_Ney_Schmalenstroeer_Drude_Heymann_et
al._2018, title={The RWTH/UPB System Combination for the CHiME 2018 Workshop},
booktitle={Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India}, author={Kitza, Markus and Michel, Wilfried and Boeddeker, Christoph
and Heitkaemper, Jens and Menne, Tobias and Schlüter, Ralf and Ney, Hermann and
Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and et al.}, year={2018}
}'
chicago: Kitza, Markus, Wilfried Michel, Christoph Boeddeker, Jens Heitkaemper,
Tobias Menne, Ralf Schlüter, Hermann Ney, et al. “The RWTH/UPB System Combination
for the CHiME 2018 Workshop.” In Proc. CHiME 2018 Workshop on Speech Processing
in Everyday Environments, Hyderabad, India, 2018.
ieee: M. Kitza et al., “The RWTH/UPB System Combination for the CHiME 2018
Workshop,” 2018.
mla: Kitza, Markus, et al. “The RWTH/UPB System Combination for the CHiME 2018 Workshop.”
Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
India, 2018.
short: 'M. Kitza, W. Michel, C. Boeddeker, J. Heitkaemper, T. Menne, R. Schlüter,
H. Ney, J. Schmalenstroeer, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc. CHiME
2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India,
2018.'
date_created: 2019-07-12T05:29:58Z
date_updated: 2023-10-26T08:12:14Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_RWTH_Paper.pdf
oa: '1'
publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
Hyderabad, India
quality_controlled: '1'
status: public
title: The RWTH/UPB System Combination for the CHiME 2018 Workshop
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11836'
abstract:
- lang: eng
text: Due to their distributed nature wireless acoustic sensor networks offer great
potential for improved signal acquisition, processing and classification for applications
such as monitoring and surveillance, home automation, or hands-free telecommunication.
To reduce the communication demand with a central server and to raise the privacy
level it is desirable to perform processing at node level. The limited processing
and memory capabilities on a sensor node, however, stand in contrast to the compute
and memory intensive deep learning algorithms used in modern speech and audio
processing. In this work, we perform benchmarking of commonly used convolutional
and recurrent neural network architectures on a Raspberry Pi based acoustic sensor
node. We show that it is possible to run medium-sized neural network topologies
used for speech enhancement and speech recognition in real time. For acoustic
event recognition, where predictions in a lower temporal resolution are sufficient,
it is even possible to run current state-of-the-art deep convolutional models
with a real-time-factor of 0:11.
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Heitkaemper J, Schmalenstroeer J, Haeb-Umbach R. Benchmarking Neural
Network Architectures for Acoustic Sensor Networks. In: ITG 2018, Oldenburg,
Germany. ; 2018.'
apa: Ebbers, J., Heitkaemper, J., Schmalenstroeer, J., & Haeb-Umbach, R. (2018).
Benchmarking Neural Network Architectures for Acoustic Sensor Networks. ITG
2018, Oldenburg, Germany.
bibtex: '@inproceedings{Ebbers_Heitkaemper_Schmalenstroeer_Haeb-Umbach_2018, title={Benchmarking
Neural Network Architectures for Acoustic Sensor Networks}, booktitle={ITG 2018,
Oldenburg, Germany}, author={Ebbers, Janek and Heitkaemper, Jens and Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Ebbers, Janek, Jens Heitkaemper, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach.
“Benchmarking Neural Network Architectures for Acoustic Sensor Networks.” In ITG
2018, Oldenburg, Germany, 2018.
ieee: J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Benchmarking
Neural Network Architectures for Acoustic Sensor Networks,” 2018.
mla: Ebbers, Janek, et al. “Benchmarking Neural Network Architectures for Acoustic
Sensor Networks.” ITG 2018, Oldenburg, Germany, 2018.
short: 'J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: ITG 2018,
Oldenburg, Germany, 2018.'
date_created: 2019-07-12T05:29:11Z
date_updated: 2023-10-26T08:12:40Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Poster.pdf
status: public
title: Benchmarking Neural Network Architectures for Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11839'
abstract:
- lang: eng
text: It has been experimentally verified that sampling rate offsets (SROs) between
the input channels of an acoustic beamformer have a detrimental effect on the
achievable SNR gains. In this paper we derive an analytic model to study the impact
of SRO on the estimation of the spatial noise covariance matrix used in MVDR beamforming.
It is shown that a perfect compensation of the SRO is impossible if the noise
covariance matrix is estimated by time averaging, even if the SRO is perfectly
known. The SRO should therefore be compensated for prior to beamformer coefficient
estimation. We present a novel scheme where SRO compensation and beamforming closely
interact, saving some computational effort compared to separate SRO adjustment
followed by acoustic beamforming.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Haeb-Umbach R. Insights into the Interplay of Sampling
Rate Offsets and MVDR Beamforming. In: ITG 2018, Oldenburg, Germany. ;
2018.'
apa: Schmalenstroeer, J., & Haeb-Umbach, R. (2018). Insights into the Interplay
of Sampling Rate Offsets and MVDR Beamforming. ITG 2018, Oldenburg, Germany.
bibtex: '@inproceedings{Schmalenstroeer_Haeb-Umbach_2018, title={Insights into the
Interplay of Sampling Rate Offsets and MVDR Beamforming}, booktitle={ITG 2018,
Oldenburg, Germany}, author={Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold},
year={2018} }'
chicago: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Insights into the Interplay
of Sampling Rate Offsets and MVDR Beamforming.” In ITG 2018, Oldenburg, Germany,
2018.
ieee: J. Schmalenstroeer and R. Haeb-Umbach, “Insights into the Interplay of Sampling
Rate Offsets and MVDR Beamforming,” 2018.
mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Insights into the Interplay
of Sampling Rate Offsets and MVDR Beamforming.” ITG 2018, Oldenburg, Germany,
2018.
short: 'J. Schmalenstroeer, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany, 2018.'
date_created: 2019-07-12T05:29:15Z
date_updated: 2023-10-26T08:12:22Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Schmalenstroeer_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
quality_controlled: '1'
status: public
title: Insights into the Interplay of Sampling Rate Offsets and MVDR Beamforming
type: conference
user_id: '460'
year: '2018'
...
---
_id: '45392'
author:
- first_name: Jennifer
full_name: Dröse, Jennifer
id: '85820'
last_name: Dröse
citation:
ama: 'Dröse J. Textaufgaben strategisch und sprachlich bewältigen lernen – Pilotstudie
zur Wirksamkeit eines Förderkonzepts. In: Fachgruppe Didaktik der Mathematik der
Universität Paderborn, ed. Beiträge zum Mathematikunterricht 2018. WTM;
2018:469-472.'
apa: Dröse, J. (2018). Textaufgaben strategisch und sprachlich bewältigen lernen
– Pilotstudie zur Wirksamkeit eines Förderkonzepts. In Fachgruppe Didaktik der
Mathematik der Universität Paderborn (Ed.), Beiträge zum Mathematikunterricht
2018 (pp. 469–472). WTM.
bibtex: '@inproceedings{Dröse_2018, place={Münster}, title={Textaufgaben strategisch
und sprachlich bewältigen lernen – Pilotstudie zur Wirksamkeit eines Förderkonzepts},
booktitle={Beiträge zum Mathematikunterricht 2018}, publisher={WTM}, author={Dröse,
Jennifer}, editor={Fachgruppe Didaktik der Mathematik der Universität Paderborn},
year={2018}, pages={469–472} }'
chicago: 'Dröse, Jennifer. “Textaufgaben strategisch und sprachlich bewältigen lernen
– Pilotstudie zur Wirksamkeit eines Förderkonzepts.” In Beiträge zum Mathematikunterricht
2018, edited by Fachgruppe Didaktik der Mathematik der Universität Paderborn,
469–72. Münster: WTM, 2018.'
ieee: J. Dröse, “Textaufgaben strategisch und sprachlich bewältigen lernen – Pilotstudie
zur Wirksamkeit eines Förderkonzepts,” in Beiträge zum Mathematikunterricht
2018, 2018, pp. 469–472.
mla: Dröse, Jennifer. “Textaufgaben strategisch und sprachlich bewältigen lernen
– Pilotstudie zur Wirksamkeit eines Förderkonzepts.” Beiträge zum Mathematikunterricht
2018, edited by Fachgruppe Didaktik der Mathematik der Universität Paderborn,
WTM, 2018, pp. 469–72.
short: 'J. Dröse, in: Fachgruppe Didaktik der Mathematik der Universität Paderborn
(Ed.), Beiträge zum Mathematikunterricht 2018, WTM, Münster, 2018, pp. 469–472.'
corporate_editor:
- Fachgruppe Didaktik der Mathematik der Universität Paderborn
date_created: 2023-05-31T08:15:12Z
date_updated: 2023-11-02T08:10:36Z
department:
- _id: '98'
extern: '1'
language:
- iso: ger
page: 469-472
place: Münster
publication: Beiträge zum Mathematikunterricht 2018
publisher: WTM
status: public
title: Textaufgaben strategisch und sprachlich bewältigen lernen – Pilotstudie zur
Wirksamkeit eines Förderkonzepts
type: conference
user_id: '85820'
year: '2018'
...
---
_id: '45393'
author:
- first_name: Jennifer
full_name: Dröse, Jennifer
id: '85820'
last_name: Dröse
- first_name: Susanne
full_name: Prediger, Susanne
last_name: Prediger
citation:
ama: Dröse J, Prediger S. Strategien für Textaufgaben fördern – mit Info-Netzen
und Formulierungsvariationen. Mathematik lehren 206. Published online 2018:8-12.
apa: Dröse, J., & Prediger, S. (2018). Strategien für Textaufgaben fördern –
mit Info-Netzen und Formulierungsvariationen. Mathematik lehren 206, 8–12.
bibtex: '@article{Dröse_Prediger_2018, title={Strategien für Textaufgaben fördern
– mit Info-Netzen und Formulierungsvariationen}, journal={Mathematik lehren 206},
author={Dröse, Jennifer and Prediger, Susanne}, year={2018}, pages={8–12} }'
chicago: Dröse, Jennifer, and Susanne Prediger. “Strategien für Textaufgaben fördern
– mit Info-Netzen und Formulierungsvariationen.” Mathematik lehren 206,
2018, 8–12.
ieee: J. Dröse and S. Prediger, “Strategien für Textaufgaben fördern – mit Info-Netzen
und Formulierungsvariationen,” Mathematik lehren 206, pp. 8–12, 2018.
mla: Dröse, Jennifer, and Susanne Prediger. “Strategien für Textaufgaben fördern
– mit Info-Netzen und Formulierungsvariationen.” Mathematik lehren 206,
2018, pp. 8–12.
short: J. Dröse, S. Prediger, Mathematik lehren 206 (2018) 8–12.
date_created: 2023-05-31T08:19:02Z
date_updated: 2023-11-02T08:12:07Z
department:
- _id: '98'
extern: '1'
language:
- iso: ger
page: 8-12
publication: Mathematik lehren 206
status: public
title: Strategien für Textaufgaben fördern – mit Info-Netzen und Formulierungsvariationen
type: journal_article
user_id: '85820'
year: '2018'
...
---
_id: '48839'
abstract:
- lang: eng
text: We analyze the effects of including local search techniques into a multi-objective
evolutionary algorithm for solving a bi-objective orienteering problem with a
single vehicle while the two conflicting objectives are minimization of travel
time and maximization of the number of visited customer locations. Experiments
are based on a large set of specifically designed problem instances with different
characteristics and it is shown that local search techniques focusing on one of
the objectives only improve the performance of the evolutionary algorithm in terms
of both objectives. The analysis also shows that local search techniques are capable
of sending locally optimal solutions to foremost fronts of the multi-objective
optimization process, and that these solutions then become the leading factors
of the evolutionary process.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Stephan
full_name: Meisel, Stephan
last_name: Meisel
- first_name: Günter
full_name: Rudolph, Günter
last_name: Rudolph
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Bossek J, Grimme C, Meisel S, Rudolph G, Trautmann H. Local Search Effects
in Bi-Objective Orienteering. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’18. Association for Computing Machinery; 2018:585–592.
doi:10.1145/3205455.3205548'
apa: Bossek, J., Grimme, C., Meisel, S., Rudolph, G., & Trautmann, H. (2018).
Local Search Effects in Bi-Objective Orienteering. Proceedings of the Genetic
and Evolutionary Computation Conference, 585–592. https://doi.org/10.1145/3205455.3205548
bibtex: '@inproceedings{Bossek_Grimme_Meisel_Rudolph_Trautmann_2018, place={New
York, NY, USA}, series={GECCO ’18}, title={Local Search Effects in Bi-Objective
Orienteering}, DOI={10.1145/3205455.3205548},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Grimme,
Christian and Meisel, Stephan and Rudolph, Günter and Trautmann, Heike}, year={2018},
pages={585–592}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob, Christian Grimme, Stephan Meisel, Günter Rudolph, and Heike
Trautmann. “Local Search Effects in Bi-Objective Orienteering.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 585–592. GECCO ’18.
New York, NY, USA: Association for Computing Machinery, 2018. https://doi.org/10.1145/3205455.3205548.'
ieee: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, and H. Trautmann, “Local Search
Effects in Bi-Objective Orienteering,” in Proceedings of the Genetic and Evolutionary
Computation Conference, 2018, pp. 585–592, doi: 10.1145/3205455.3205548.'
mla: Bossek, Jakob, et al. “Local Search Effects in Bi-Objective Orienteering.”
Proceedings of the Genetic and Evolutionary Computation Conference, Association
for Computing Machinery, 2018, pp. 585–592, doi:10.1145/3205455.3205548.
short: 'J. Bossek, C. Grimme, S. Meisel, G. Rudolph, H. Trautmann, in: Proceedings
of the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, New York, NY, USA, 2018, pp. 585–592.'
date_created: 2023-11-14T15:58:51Z
date_updated: 2023-12-13T10:42:14Z
department:
- _id: '819'
doi: 10.1145/3205455.3205548
extern: '1'
keyword:
- combinatorial optimization
- metaheuristics
- multi-objective optimization
- orienteering
- transportation
language:
- iso: eng
page: 585–592
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-5618-3
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Local Search Effects in Bi-Objective Orienteering
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48867'
abstract:
- lang: eng
text: Assessing the performance of stochastic optimization algorithms in the field
of multi-objective optimization is of utmost importance. Besides the visual comparison
of the obtained approximation sets, more sophisticated methods have been proposed
in the last decade, e. g., a variety of quantitative performance indicators or
statistical tests. In this paper, we present tools implemented in the R package
ecr, which assist in performing comprehensive and sound comparison and evaluation
of multi-objective evolutionary algorithms following recommendations from the
literature.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. In: Proceedings of the Genetic and Evolutionary Computation
Conference Companion. GECCO ’18. Association for Computing Machinery; 2018:1350–1356.
doi:10.1145/3205651.3208312'
apa: Bossek, J. (2018). Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr. Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 1350–1356. https://doi.org/10.1145/3205651.3208312
bibtex: '@inproceedings{Bossek_2018, place={New York, NY, USA}, series={GECCO ’18},
title={Performance Assessment of Multi-Objective Evolutionary Algorithms with
the R Package ecr}, DOI={10.1145/3205651.3208312},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Bossek, Jakob},
year={2018}, pages={1350–1356}, collection={GECCO ’18} }'
chicago: 'Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary
Algorithms with the R Package Ecr.” In Proceedings of the Genetic and Evolutionary
Computation Conference Companion, 1350–1356. GECCO ’18. New York, NY, USA:
Association for Computing Machinery, 2018. https://doi.org/10.1145/3205651.3208312.'
ieee: 'J. Bossek, “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package ecr,” in Proceedings of the Genetic and Evolutionary Computation
Conference Companion, 2018, pp. 1350–1356, doi: 10.1145/3205651.3208312.'
mla: Bossek, Jakob. “Performance Assessment of Multi-Objective Evolutionary Algorithms
with the R Package Ecr.” Proceedings of the Genetic and Evolutionary Computation
Conference Companion, Association for Computing Machinery, 2018, pp. 1350–1356,
doi:10.1145/3205651.3208312.
short: 'J. Bossek, in: Proceedings of the Genetic and Evolutionary Computation Conference
Companion, Association for Computing Machinery, New York, NY, USA, 2018, pp. 1350–1356.'
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:46:04Z
department:
- _id: '819'
doi: 10.1145/3205651.3208312
extern: '1'
keyword:
- evolutionary optimization
- performance assessment
- software-tools
language:
- iso: eng
page: 1350–1356
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’18
status: public
title: Performance Assessment of Multi-Objective Evolutionary Algorithms with the
R Package ecr
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48885'
abstract:
- lang: eng
text: Performance comparisons of optimization algorithms are heavily influenced
by the underlying indicator(s). In this paper we investigate commonly used performance
indicators for single-objective stochastic solvers, such as the Penalized Average
Runtime (e.g., PAR10) or the Expected Running Time (ERT), based on exemplary benchmark
performances of state-of-the-art inexact TSP solvers. Thereby, we introduce a
methodology for analyzing the effects of (usually heuristically set) indicator
parametrizations - such as the penalty factor and the method used for aggregating
across multiple runs - w.r.t. the robustness of the considered optimization algorithms.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: 'Kerschke P, Bossek J, Trautmann H. Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers. In: Proceedings
of the Genetic and Evolutionary Computation Conference Companion. GECCO’18.
Association for Computing Machinery; 2018:1737–1744. doi:10.1145/3205651.3208233'
apa: 'Kerschke, P., Bossek, J., & Trautmann, H. (2018). Parameterization of
State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact TSP
Solvers. Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. https://doi.org/10.1145/3205651.3208233'
bibtex: '@inproceedings{Kerschke_Bossek_Trautmann_2018, place={New York, NY, USA},
series={GECCO’18}, title={Parameterization of State-of-the-Art Performance Indicators:
A Robustness Study Based on Inexact TSP Solvers}, DOI={10.1145/3205651.3208233},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference
Companion}, publisher={Association for Computing Machinery}, author={Kerschke,
Pascal and Bossek, Jakob and Trautmann, Heike}, year={2018}, pages={1737–1744},
collection={GECCO’18} }'
chicago: 'Kerschke, Pascal, Jakob Bossek, and Heike Trautmann. “Parameterization
of State-of-the-Art Performance Indicators: A Robustness Study Based on Inexact
TSP Solvers.” In Proceedings of the Genetic and Evolutionary Computation Conference
Companion, 1737–1744. GECCO’18. New York, NY, USA: Association for Computing
Machinery, 2018. https://doi.org/10.1145/3205651.3208233.'
ieee: 'P. Kerschke, J. Bossek, and H. Trautmann, “Parameterization of State-of-the-Art
Performance Indicators: A Robustness Study Based on Inexact TSP Solvers,” in Proceedings
of the Genetic and Evolutionary Computation Conference Companion, 2018, pp.
1737–1744, doi: 10.1145/3205651.3208233.'
mla: 'Kerschke, Pascal, et al. “Parameterization of State-of-the-Art Performance
Indicators: A Robustness Study Based on Inexact TSP Solvers.” Proceedings of
the Genetic and Evolutionary Computation Conference Companion, Association
for Computing Machinery, 2018, pp. 1737–1744, doi:10.1145/3205651.3208233.'
short: 'P. Kerschke, J. Bossek, H. Trautmann, in: Proceedings of the Genetic and
Evolutionary Computation Conference Companion, Association for Computing Machinery,
New York, NY, USA, 2018, pp. 1737–1744.'
date_created: 2023-11-14T15:58:59Z
date_updated: 2023-12-13T10:48:38Z
department:
- _id: '819'
doi: 10.1145/3205651.3208233
extern: '1'
keyword:
- algorithm selection
- optimization
- performance measures
- transportation
- travelling salesperson problem
language:
- iso: eng
page: 1737–1744
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference Companion
publication_identifier:
isbn:
- 978-1-4503-5764-7
publisher: Association for Computing Machinery
series_title: GECCO’18
status: public
title: 'Parameterization of State-of-the-Art Performance Indicators: A Robustness
Study Based on Inexact TSP Solvers'
type: conference
user_id: '102979'
year: '2018'
...
---
_id: '48880'
author:
- first_name: Christian
full_name: Grimme, Christian
last_name: Grimme
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: Grimme C, Bossek J. Einführung in Die Optimierung - Konzepte, Methoden Und
Anwendungen. Springer Vieweg; 2018. doi:10.1007/978-3-658-21151-6
apa: Grimme, C., & Bossek, J. (2018). Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen. Springer Vieweg. https://doi.org/10.1007/978-3-658-21151-6
bibtex: '@book{Grimme_Bossek_2018, title={Einführung in die Optimierung - Konzepte,
Methoden und Anwendungen}, DOI={10.1007/978-3-658-21151-6},
publisher={Springer Vieweg}, author={Grimme, Christian and Bossek, Jakob}, year={2018}
}'
chicago: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung -
Konzepte, Methoden Und Anwendungen. Springer Vieweg, 2018. https://doi.org/10.1007/978-3-658-21151-6.
ieee: C. Grimme and J. Bossek, Einführung in die Optimierung - Konzepte, Methoden
und Anwendungen. Springer Vieweg, 2018.
mla: Grimme, Christian, and Jakob Bossek. Einführung in Die Optimierung - Konzepte,
Methoden Und Anwendungen. Springer Vieweg, 2018, doi:10.1007/978-3-658-21151-6.
short: C. Grimme, J. Bossek, Einführung in Die Optimierung - Konzepte, Methoden
Und Anwendungen, Springer Vieweg, 2018.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:47:57Z
department:
- _id: '819'
doi: 10.1007/978-3-658-21151-6
extern: '1'
language:
- iso: eng
publication_identifier:
isbn:
- 978-3-658-21150-9
publisher: Springer Vieweg
status: public
title: Einführung in die Optimierung - Konzepte, Methoden und Anwendungen
type: book
user_id: '102979'
year: '2018'
...
---
_id: '48884'
abstract:
- lang: eng
text: The Travelling Salesperson Problem (TSP) is one of the best-studied NP-hard
problems. Over the years, many different solution approaches and solvers have
been developed. For the first time, we directly compare five state-of-the-art
inexact solvers\textemdash namely, LKH, EAX, restart variants of those, and MAOS\textemdash
on a large set of well-known benchmark instances and demonstrate complementary
performance, in that different instances may be solved most effectively by different
algorithms. We leverage this complementarity to build an algorithm selector, which
selects the best TSP solver on a per-instance basis and thus achieves significantly
improved performance compared to the single best solver, representing an advance
in the state of the art in solving the Euclidean TSP. Our in-depth analysis of
the selectors provides insight into what drives this performance improvement.
author:
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Lars
full_name: Kotthoff, Lars
last_name: Kotthoff
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Holger H.
full_name: Hoos, Holger H.
last_name: Hoos
- first_name: Heike
full_name: Trautmann, Heike
last_name: Trautmann
citation:
ama: Kerschke P, Kotthoff L, Bossek J, Hoos HH, Trautmann H. Leveraging TSP Solver
Complementarity through Machine Learning. Evolutionary Computation. 2018;26(4):597–620.
doi:10.1162/evco_a_00215
apa: Kerschke, P., Kotthoff, L., Bossek, J., Hoos, H. H., & Trautmann, H. (2018).
Leveraging TSP Solver Complementarity through Machine Learning. Evolutionary
Computation, 26(4), 597–620. https://doi.org/10.1162/evco_a_00215
bibtex: '@article{Kerschke_Kotthoff_Bossek_Hoos_Trautmann_2018, title={Leveraging
TSP Solver Complementarity through Machine Learning}, volume={26}, DOI={10.1162/evco_a_00215},
number={4}, journal={Evolutionary Computation}, author={Kerschke, Pascal and Kotthoff,
Lars and Bossek, Jakob and Hoos, Holger H. and Trautmann, Heike}, year={2018},
pages={597–620} }'
chicago: 'Kerschke, Pascal, Lars Kotthoff, Jakob Bossek, Holger H. Hoos, and Heike
Trautmann. “Leveraging TSP Solver Complementarity through Machine Learning.” Evolutionary
Computation 26, no. 4 (2018): 597–620. https://doi.org/10.1162/evco_a_00215.'
ieee: 'P. Kerschke, L. Kotthoff, J. Bossek, H. H. Hoos, and H. Trautmann, “Leveraging
TSP Solver Complementarity through Machine Learning,” Evolutionary Computation,
vol. 26, no. 4, pp. 597–620, 2018, doi: 10.1162/evco_a_00215.'
mla: Kerschke, Pascal, et al. “Leveraging TSP Solver Complementarity through Machine
Learning.” Evolutionary Computation, vol. 26, no. 4, 2018, pp. 597–620,
doi:10.1162/evco_a_00215.
short: P. Kerschke, L. Kotthoff, J. Bossek, H.H. Hoos, H. Trautmann, Evolutionary
Computation 26 (2018) 597–620.
date_created: 2023-11-14T15:58:58Z
date_updated: 2023-12-13T10:51:26Z
department:
- _id: '819'
doi: 10.1162/evco_a_00215
intvolume: ' 26'
issue: '4'
keyword:
- automated algorithm selection
- machine learning.
- performance modeling
- Travelling Salesperson Problem
language:
- iso: eng
page: 597–620
publication: Evolutionary Computation
publication_identifier:
issn:
- 1063-6560
status: public
title: Leveraging TSP Solver Complementarity through Machine Learning
type: journal_article
user_id: '102979'
volume: 26
year: '2018'
...
---
_id: '48866'
abstract:
- lang: eng
text: 'Bossek, (2018). grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528, https://doi.org/10.21105/joss.00528'
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
citation:
ama: 'Bossek J. Grapherator: A Modular Multi-Step Graph Generator. Journal of
Open Source Software. 2018;3(22):528. doi:10.21105/joss.00528'
apa: 'Bossek, J. (2018). Grapherator: A Modular Multi-Step Graph Generator. Journal
of Open Source Software, 3(22), 528. https://doi.org/10.21105/joss.00528'
bibtex: '@article{Bossek_2018, title={Grapherator: A Modular Multi-Step Graph Generator},
volume={3}, DOI={10.21105/joss.00528},
number={22}, journal={Journal of Open Source Software}, author={Bossek, Jakob},
year={2018}, pages={528} }'
chicago: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software 3, no. 22 (2018): 528. https://doi.org/10.21105/joss.00528.'
ieee: 'J. Bossek, “Grapherator: A Modular Multi-Step Graph Generator,” Journal
of Open Source Software, vol. 3, no. 22, p. 528, 2018, doi: 10.21105/joss.00528.'
mla: 'Bossek, Jakob. “Grapherator: A Modular Multi-Step Graph Generator.” Journal
of Open Source Software, vol. 3, no. 22, 2018, p. 528, doi:10.21105/joss.00528.'
short: J. Bossek, Journal of Open Source Software 3 (2018) 528.
date_created: 2023-11-14T15:58:56Z
date_updated: 2023-12-13T10:51:50Z
department:
- _id: '819'
doi: 10.21105/joss.00528
intvolume: ' 3'
issue: '22'
language:
- iso: eng
page: '528'
publication: Journal of Open Source Software
publication_identifier:
issn:
- 2475-9066
status: public
title: 'Grapherator: A Modular Multi-Step Graph Generator'
type: journal_article
user_id: '102979'
volume: 3
year: '2018'
...
---
_id: '31374'
author:
- first_name: Max
full_name: Hoffmann, Max
id: '32202'
last_name: Hoffmann
orcid: 0000-0002-6964-7123
citation:
ama: Hoffmann M. Konzeption von fachmathematischen Schnittstellenmodulen für
Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis.
Vol 18-06.; 2018.
apa: Hoffmann, M. (2018). Konzeption von fachmathematischen Schnittstellenmodulen
für Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis.
(Vols. 18–06).
bibtex: '@book{Hoffmann_2018, series={khdm-Report}, title={Konzeption von fachmathematischen
Schnittstellenmodulen für Lehramtsstudierende am Beispiel ausgewählter Themen
der höheren Analysis.}, volume={18–06}, author={Hoffmann, Max}, year={2018}, collection={khdm-Report}
}'
chicago: Hoffmann, Max. Konzeption von fachmathematischen Schnittstellenmodulen
für Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis.
Vol. 18–06. khdm-Report, 2018.
ieee: M. Hoffmann, Konzeption von fachmathematischen Schnittstellenmodulen für
Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis.,
vol. 18–06. 2018.
mla: Hoffmann, Max. Konzeption von fachmathematischen Schnittstellenmodulen für
Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis.
2018.
short: M. Hoffmann, Konzeption von fachmathematischen Schnittstellenmodulen für
Lehramtsstudierende am Beispiel ausgewählter Themen der höheren Analysis., 2018.
date_created: 2022-05-22T14:51:16Z
date_updated: 2024-02-19T06:21:04Z
department:
- _id: '97'
language:
- iso: ger
main_file_link:
- open_access: '1'
url: http://nbn-resolving.de/urn:nbn:de:hebis:34-2017110153692
oa: '1'
publication_status: published
series_title: khdm-Report
status: public
supervisor:
- first_name: Rolf
full_name: Biehler, Rolf
last_name: Biehler
- first_name: Joachim
full_name: Hilgert, Joachim
id: '220'
last_name: Hilgert
title: Konzeption von fachmathematischen Schnittstellenmodulen für Lehramtsstudierende
am Beispiel ausgewählter Themen der höheren Analysis.
type: mastersthesis
user_id: '49063'
volume: ' 18 - 06'
year: '2018'
...