---
_id: '6512'
abstract:
- lang: eng
text: Scheduling problems are essential for decision making in many academic disciplines,
including operations management, computer science, and information systems. Since
many scheduling problems are NP-hard in the strong sense, there is only limited
research on exact algorithms and how their efficiency scales when implemented
on parallel computing architectures. We address this gap by (1) adapting an exact
branch-and-price algorithm to a parallel machine scheduling problem on unrelated
machines with sequence- and machine-dependent setup times, (2) parallelizing the
adapted algorithm by implementing a distributed-memory parallelization with a
master/worker approach, and (3) conducting extensive computational experiments
using up to 960 MPI processes on a modern high performance computing cluster.
With our experiments, we show that the efficiency of our parallelization approach
can lead to superlinear speedup but can vary substantially between instances.
We further show that the wall time of serial execution can be substantially reduced
through our parallelization, in some cases from 94 hours to less than six minutes
when our algorithm is executed on 960 processes.
author:
- first_name: Gerhard
full_name: Rauchecker, Gerhard
last_name: Rauchecker
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Rauchecker G, Schryen G. Using High Performance Computing for Unrelated Parallel
Machine Scheduling with Sequence-Dependent Setup Times: Development and Computational
Evaluation of a Parallel Branch-and-Price Algorithm. Computers & Operations
Research. 2019;(104):338-357.'
apa: 'Rauchecker, G., & Schryen, G. (2019). Using High Performance Computing
for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times:
Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm.
Computers & Operations Research, (104), 338–357.'
bibtex: '@article{Rauchecker_Schryen_2019, title={Using High Performance Computing
for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times:
Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm},
number={104}, journal={Computers & Operations Research}, publisher={Elsevier},
author={Rauchecker, Gerhard and Schryen, Guido}, year={2019}, pages={338–357}
}'
chicago: 'Rauchecker, Gerhard, and Guido Schryen. “Using High Performance Computing
for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times:
Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm.”
Computers & Operations Research, no. 104 (2019): 338–57.'
ieee: 'G. Rauchecker and G. Schryen, “Using High Performance Computing for Unrelated
Parallel Machine Scheduling with Sequence-Dependent Setup Times: Development and
Computational Evaluation of a Parallel Branch-and-Price Algorithm,” Computers
& Operations Research, no. 104, pp. 338–357, 2019.'
mla: 'Rauchecker, Gerhard, and Guido Schryen. “Using High Performance Computing
for Unrelated Parallel Machine Scheduling with Sequence-Dependent Setup Times:
Development and Computational Evaluation of a Parallel Branch-and-Price Algorithm.”
Computers & Operations Research, no. 104, Elsevier, 2019, pp. 338–57.'
short: G. Rauchecker, G. Schryen, Computers & Operations Research (2019) 338–357.
date_created: 2019-01-08T13:50:44Z
date_updated: 2022-01-06T07:03:08Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2019-01-08T14:03:53Z
date_updated: 2019-01-08T14:03:53Z
file_id: '6513'
file_name: cor-parallel-bp-for-upmsp.pdf
file_size: 4153528
relation: main_file
file_date_updated: 2019-01-08T14:03:53Z
has_accepted_license: '1'
issue: '104'
keyword:
- parallel machine scheduling with setup times
- parallel branch-and-price algorithm
- high performance computing
- master/worker parallelization
language:
- iso: eng
oa: '1'
page: 338-357
publication: Computers & Operations Research
publisher: Elsevier
status: public
title: 'Using High Performance Computing for Unrelated Parallel Machine Scheduling
with Sequence-Dependent Setup Times: Development and Computational Evaluation of
a Parallel Branch-and-Price Algorithm'
type: journal_article
user_id: '61579'
year: '2019'
...
---
_id: '6514'
abstract:
- lang: eng
text: Recommender Agents (RAs) facilitate consumers’ online purchase decisions for
complex, multi-attribute products. As not all combinations of attribute levels
can be obtained, users are forced into trade-offs. The exposure of trade-offs
in a RA has been found to affect consumers’ perceptions. However, little is known
about how different preference elicitation methods in RAs affect consumers by
varying degrees of trade-off exposure. We propose a research model that investigates
how different levels of trade-off exposure cognitively and affectively influence
consumers’ satisfaction with RAs. We operationalize these levels in three different
RA types and test our hypotheses in a laboratory experiment with 116 participants.
Our results indicate that with increasing tradeoff exposure, perceived enjoyment
and perceived control follow an inverted Ushaped relationship. Hence, RAs using
preference elicitation methods with medium trade-off exposure yield highest consumer
satisfaction. This contributes to the understanding of trade-offs in RAs and provides
valuable implications to e-commerce practitioners.
author:
- first_name: Veronika
full_name: Schuhbeck, Veronika
last_name: Schuhbeck
- first_name: Nils
full_name: Siegfried, Nils
last_name: Siegfried
- first_name: Verena
full_name: Dorner, Verena
last_name: Dorner
- first_name: Alexander
full_name: Benlian, Alexander
last_name: Benlian
- first_name: Michael
full_name: Scholz, Michael
last_name: Scholz
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
citation:
ama: 'Schuhbeck V, Siegfried N, Dorner V, Benlian A, Scholz M, Schryen G. Walking
the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer Satisfaction
in Recommender Agents. In: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik.
Siegen, Germany; 2019:55-64.'
apa: 'Schuhbeck, V., Siegfried, N., Dorner, V., Benlian, A., Scholz, M., & Schryen,
G. (2019). Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher
Consumer Satisfaction in Recommender Agents. In Proceedings of the 14. Internationale
Tagung Wirtschaftsinformatik (pp. 55–64). Siegen, Germany.'
bibtex: '@inproceedings{Schuhbeck_Siegfried_Dorner_Benlian_Scholz_Schryen_2019,
place={Siegen, Germany}, title={Walking the Middle Path: How Medium Trade-off
Exposure Leads to Higher Consumer Satisfaction in Recommender Agents}, booktitle={Proceedings
of the 14. Internationale Tagung Wirtschaftsinformatik}, author={Schuhbeck, Veronika
and Siegfried, Nils and Dorner, Verena and Benlian, Alexander and Scholz, Michael
and Schryen, Guido}, year={2019}, pages={55–64} }'
chicago: 'Schuhbeck, Veronika, Nils Siegfried, Verena Dorner, Alexander Benlian,
Michael Scholz, and Guido Schryen. “Walking the Middle Path: How Medium Trade-off
Exposure Leads to Higher Consumer Satisfaction in Recommender Agents.” In Proceedings
of the 14. Internationale Tagung Wirtschaftsinformatik, 55–64. Siegen, Germany,
2019.'
ieee: 'V. Schuhbeck, N. Siegfried, V. Dorner, A. Benlian, M. Scholz, and G. Schryen,
“Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer
Satisfaction in Recommender Agents,” in Proceedings of the 14. Internationale
Tagung Wirtschaftsinformatik, Siegen, Germany, 2019, pp. 55–64.'
mla: 'Schuhbeck, Veronika, et al. “Walking the Middle Path: How Medium Trade-off
Exposure Leads to Higher Consumer Satisfaction in Recommender Agents.” Proceedings
of the 14. Internationale Tagung Wirtschaftsinformatik, 2019, pp. 55–64.'
short: 'V. Schuhbeck, N. Siegfried, V. Dorner, A. Benlian, M. Scholz, G. Schryen,
in: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik, Siegen,
Germany, 2019, pp. 55–64.'
conference:
end_date: 2019-02-27
location: Siegen, Germany
name: 14th International Conference on Wirtschaftsinformatik
start_date: 2019-02-24
date_created: 2019-01-08T14:06:32Z
date_updated: 2022-01-06T07:03:09Z
ddc:
- '000'
department:
- _id: '277'
file:
- access_level: closed
content_type: application/pdf
creator: hsiemes
date_created: 2019-01-08T14:07:17Z
date_updated: 2021-08-13T13:26:11Z
file_id: '6515'
file_name: WALKING THE MIDDLE PATH.pdf
file_size: 371490
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: hsiemes
date_created: 2021-08-13T13:25:53Z
date_updated: 2021-08-13T13:25:53Z
file_id: '23393'
file_name: Walking the Middle Path_ How Medium Trade-Off Exposure Leads to H.pdf
file_size: 331001
relation: main_file
file_date_updated: 2021-08-13T13:26:11Z
has_accepted_license: '1'
keyword:
- Recommender Agents
- Preference Elicitation Method
- Trade-off Exposure
- Customer Satisfaction
language:
- iso: eng
oa: '1'
page: 55-64
place: Siegen, Germany
publication: Proceedings of the 14. Internationale Tagung Wirtschaftsinformatik
status: public
title: 'Walking the Middle Path: How Medium Trade-off Exposure Leads to Higher Consumer
Satisfaction in Recommender Agents'
type: conference
user_id: '61579'
year: '2019'
...
---
_id: '6860'
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
citation:
ama: 'Afifi H, Karl H. Power Allocation with a Wireless Multi-cast Aware Routing
for Virtual Network Embedding. In: 2019 16th IEEE Annual Consumer Communications
& Networking Conference (CCNC2019). Las Vegas: IEEE.'
apa: 'Afifi, H., & Karl, H. (n.d.). Power Allocation with a Wireless Multi-cast
Aware Routing for Virtual Network Embedding. In 2019 16th IEEE Annual Consumer
Communications & Networking Conference (CCNC2019). Las Vegas: IEEE.'
bibtex: '@inproceedings{Afifi_Karl, place={Las Vegas}, title={Power Allocation with
a Wireless Multi-cast Aware Routing for Virtual Network Embedding}, booktitle={2019
16th IEEE Annual Consumer Communications & Networking Conference (CCNC2019)},
publisher={IEEE}, author={Afifi, Haitham and Karl, Holger} }'
chicago: 'Afifi, Haitham, and Holger Karl. “Power Allocation with a Wireless Multi-Cast
Aware Routing for Virtual Network Embedding.” In 2019 16th IEEE Annual Consumer
Communications & Networking Conference (CCNC2019). Las Vegas: IEEE, n.d.'
ieee: H. Afifi and H. Karl, “Power Allocation with a Wireless Multi-cast Aware Routing
for Virtual Network Embedding,” in 2019 16th IEEE Annual Consumer Communications
& Networking Conference (CCNC2019).
mla: Afifi, Haitham, and Holger Karl. “Power Allocation with a Wireless Multi-Cast
Aware Routing for Virtual Network Embedding.” 2019 16th IEEE Annual Consumer
Communications & Networking Conference (CCNC2019), IEEE.
short: 'H. Afifi, H. Karl, in: 2019 16th IEEE Annual Consumer Communications &
Networking Conference (CCNC2019), IEEE, Las Vegas, n.d.'
date_created: 2019-01-17T15:51:34Z
date_updated: 2022-01-06T07:03:22Z
ddc:
- '000'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: hafifi
date_created: 2019-01-17T15:49:37Z
date_updated: 2019-01-17T15:49:37Z
file_id: '6861'
file_name: globecom.pdf
file_size: 320283
relation: main_file
file_date_updated: 2019-01-17T15:49:37Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
place: Las Vegas
project:
- _id: '27'
name: 'Akustische Sensornetzwerke - Teilprojekt '
- _id: '27'
name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
über funkbasierte Sensornetzwerke
publication: 2019 16th IEEE Annual Consumer Communications & Networking Conference
(CCNC2019)
publication_status: accepted
publisher: IEEE
status: public
title: Power Allocation with a Wireless Multi-cast Aware Routing for Virtual Network
Embedding
type: conference
user_id: '65718'
year: '2019'
...
---
_id: '16847'
abstract:
- lang: eng
text: In this work we describe our results achieved in the ProtestNews Lab at CLEF
2019. To tackle the problems of event sentence detection and event extraction
we decided to use contextualized string embeddings. The models were trained on
a data corpus collected from Indian news sources, but evaluated on data obtained
from news sources from other countries as well, such as China. Our models have
obtained competitive results and have scored 3rd in the event sentence detection
task and 1st in the event extraction task based on average F1-scores for different
test datasets.
author:
- first_name: Gabriella
full_name: Skitalinskaya, Gabriella
last_name: Skitalinskaya
- first_name: Jonas
full_name: Klaff, Jonas
last_name: Klaff
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
citation:
ama: 'Skitalinskaya G, Klaff J, Spliethöver M. CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction. Vol 2380.
Lugano, Switzerland; 2019.'
apa: 'Skitalinskaya, G., Klaff, J., & Spliethöver, M. (2019). CLEF ProtestNews
Lab 2019: Contextualized Word Embeddings for Event Sentence Detection and Event
Extraction (Vol. 2380). Lugano, Switzerland.'
bibtex: '@book{Skitalinskaya_Klaff_Spliethöver_2019, place={Lugano, Switzerland},
series={CEUR Workshop Proceedings}, title={CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction}, volume={2380},
author={Skitalinskaya, Gabriella and Klaff, Jonas and Spliethöver, Maximilian},
year={2019}, collection={CEUR Workshop Proceedings} }'
chicago: 'Skitalinskaya, Gabriella, Jonas Klaff, and Maximilian Spliethöver. CLEF
ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence Detection
and Event Extraction. Vol. 2380. CEUR Workshop Proceedings. Lugano, Switzerland,
2019.'
ieee: 'G. Skitalinskaya, J. Klaff, and M. Spliethöver, CLEF ProtestNews Lab 2019:
Contextualized Word Embeddings for Event Sentence Detection and Event Extraction,
vol. 2380. Lugano, Switzerland, 2019.'
mla: 'Skitalinskaya, Gabriella, et al. CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction. Vol. 2380,
2019.'
short: 'G. Skitalinskaya, J. Klaff, M. Spliethöver, CLEF ProtestNews Lab 2019: Contextualized
Word Embeddings for Event Sentence Detection and Event Extraction, Lugano, Switzerland,
2019.'
date_created: 2020-04-23T15:18:40Z
date_updated: 2022-01-06T06:52:57Z
extern: '1'
intvolume: ' 2380'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ceur-ws.org/Vol-2380/paper_118.pdf
oa: '1'
page: '7'
place: Lugano, Switzerland
report_number: '118'
series_title: CEUR Workshop Proceedings
status: public
title: 'CLEF ProtestNews Lab 2019: Contextualized Word Embeddings for Event Sentence
Detection and Event Extraction'
type: report
user_id: '84035'
volume: 2380
year: '2019'
...
---
_id: '11965'
abstract:
- lang: eng
text: 'We present an unsupervised training approach for a neural network-based mask
estimator in an acoustic beamforming application. The network is trained to maximize
a likelihood criterion derived from a spatial mixture model of the observations.
It is trained from scratch without requiring any parallel data consisting of degraded
input and clean training targets. Thus, training can be carried out on real recordings
of noisy speech rather than simulated ones. In contrast to previous work on unsupervised
training of neural mask estimators, our approach avoids the need for a possibly
pre-trained teacher model entirely. We demonstrate the effectiveness of our approach
by speech recognition experiments on two different datasets: one mainly deteriorated
by noise (CHiME 4) and one by reverberation (REVERB). The results show that the
performance of the proposed system is on par with a supervised system using oracle
target masks for training and with a system trained using a model-based teacher.'
author:
- 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: 'Drude L, Heymann J, Haeb-Umbach R. Unsupervised training of neural mask-based
beamforming. In: INTERSPEECH 2019, Graz, Austria. ; 2019.'
apa: Drude, L., Heymann, J., & Haeb-Umbach, R. (2019). Unsupervised training
of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria.
bibtex: '@inproceedings{Drude_Heymann_Haeb-Umbach_2019, title={Unsupervised training
of neural mask-based beamforming}, booktitle={INTERSPEECH 2019, Graz, Austria},
author={Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2019}
}'
chicago: Drude, Lukas, Jahn Heymann, and Reinhold Haeb-Umbach. “Unsupervised Training
of Neural Mask-Based Beamforming.” In INTERSPEECH 2019, Graz, Austria,
2019.
ieee: L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural
mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of Neural Mask-Based Beamforming.”
INTERSPEECH 2019, Graz, Austria, 2019.
short: 'L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria,
2019.'
date_created: 2019-07-18T09:11:39Z
date_updated: 2022-01-06T06:51:14Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-13T06:36:44Z
date_updated: 2019-08-13T06:41:35Z
file_id: '12914'
file_name: INTERSPEECH_2019_Drude_Paper.pdf
file_size: 223413
relation: main_file
file_date_updated: 2019-08-13T06:41:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2019, Graz, Austria
status: public
title: Unsupervised training of neural mask-based beamforming
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12874'
abstract:
- lang: eng
text: We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar performance
as the multi-channel teacher in terms of word error rates and (b) initializing
the spatial clustering approach with the deep clustering result yields a relative
word error rate reduction of 26% over the unsupervised teacher.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Daniel
full_name: Hasenklever, Daniel
last_name: Hasenklever
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering
Model for Multichannel Blind Source Separation. In: ICASSP 2019, Brighton,
UK. ; 2019.'
apa: Drude, L., Hasenklever, D., & Haeb-Umbach, R. (2019). Unsupervised Training
of a Deep Clustering Model for Multichannel Blind Source Separation. In ICASSP
2019, Brighton, UK.
bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation},
booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel
and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation.”
In ICASSP 2019, Brighton, UK, 2019.
ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a
Deep Clustering Model for Multichannel Blind Source Separation,” in ICASSP
2019, Brighton, UK, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for
Multichannel Blind Source Separation.” ICASSP 2019, Brighton, UK, 2019.
short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK,
2019.'
date_created: 2019-07-23T07:37:54Z
date_updated: 2022-01-06T06:51:21Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-14T07:19:13Z
date_updated: 2019-08-14T07:19:13Z
file_id: '12925'
file_name: ICASSP_2019_Drude_Paper.pdf
file_size: 368225
relation: main_file
file_date_updated: 2019-08-14T07:19:13Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source
Separation
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12875'
abstract:
- lang: eng
text: Signal dereverberation using the Weighted Prediction Error (WPE) method has
been proven to be an effective means to raise the accuracy of far-field speech
recognition. First proposed as an iterative algorithm, follow-up works have reformulated
it as a recursive least squares algorithm and therefore enabled its use in online
applications. For this algorithm, the estimation of the power spectral density
(PSD) of the anechoic signal plays an important role and strongly influences its
performance. Recently, we showed that using a neural network PSD estimator leads
to improved performance for online automatic speech recognition. This, however,
comes at a price. To train the network, we require parallel data, i.e., utterances
simultaneously available in clean and reverberated form. Here we propose to overcome
this limitation by training the network jointly with the acoustic model of the
speech recognizer. To be specific, the gradients computed from the cross-entropy
loss between the target senone sequence and the acoustic model network output
is backpropagated through the complex-valued dereverberation filter estimation
to the neural network for PSD estimation. Evaluation on two databases demonstrates
improved performance for on-line processing scenarios while imposing fewer requirements
on the available training data and thus widening the range of applications.
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
citation:
ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Joint Optimization
of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online
ASR. In: ICASSP 2019, Brighton, UK. ; 2019.'
apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T.
(2019). Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR. In ICASSP 2019, Brighton, UK.
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2019, title={Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann, Jahn
and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani,
Tomohiro}, year={2019} }'
chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and
Tomohiro Nakatani. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” In ICASSP 2019, Brighton, UK,
2019.
ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR,” in ICASSP 2019, Brighton, UK, 2019.
mla: Heymann, Jahn, et al. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” ICASSP 2019, Brighton, UK, 2019.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP
2019, Brighton, UK, 2019.'
date_created: 2019-07-23T07:42:26Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-12-17T07:28:06Z
date_updated: 2019-12-17T07:28:06Z
file_id: '15334'
file_name: ICASSP_2019_Heymann_Paper.pdf
file_size: 199109
relation: main_file
file_date_updated: 2019-12-17T07:28:06Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12876'
abstract:
- lang: eng
text: In this paper, we present libDirectional, a MATLAB library for directional
statistics and directional estimation. It supports a variety of commonly used
distributions on the unit circle, such as the von Mises, wrapped normal, and wrapped
Cauchy distributions. Furthermore, various distributions on higher-dimensional
manifolds such as the unit hypersphere and the hypertorus are available. Based
on these distributions, several recursive filtering algorithms in libDirectional
allow estimation on these manifolds. The functionality is implemented in a clear,
well-documented, and object-oriented structure that is both easy to use and easy
to extend.
author:
- first_name: Gerhard
full_name: Kurz, Gerhard
last_name: Kurz
- first_name: Igor
full_name: Gilitschenski, Igor
last_name: Gilitschenski
- first_name: Florian
full_name: Pfaff, Florian
last_name: Pfaff
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Uwe D.
full_name: Hanebeck, Uwe D.
last_name: Hanebeck
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Roland Y.
full_name: Siegwart, Roland Y.
last_name: Siegwart
citation:
ama: 'Kurz G, Gilitschenski I, Pfaff F, et al. Directional Statistics and Filtering
Using libDirectional. In: Journal of Statistical Software 89(4). ; 2019.'
apa: Kurz, G., Gilitschenski, I., Pfaff, F., Drude, L., Hanebeck, U. D., Haeb-Umbach,
R., & Siegwart, R. Y. (2019). Directional Statistics and Filtering Using libDirectional.
In Journal of Statistical Software 89(4).
bibtex: '@inproceedings{Kurz_Gilitschenski_Pfaff_Drude_Hanebeck_Haeb-Umbach_Siegwart_2019,
title={Directional Statistics and Filtering Using libDirectional}, booktitle={Journal
of Statistical Software 89(4)}, author={Kurz, Gerhard and Gilitschenski, Igor
and Pfaff, Florian and Drude, Lukas and Hanebeck, Uwe D. and Haeb-Umbach, Reinhold
and Siegwart, Roland Y.}, year={2019} }'
chicago: Kurz, Gerhard, Igor Gilitschenski, Florian Pfaff, Lukas Drude, Uwe D. Hanebeck,
Reinhold Haeb-Umbach, and Roland Y. Siegwart. “Directional Statistics and Filtering
Using LibDirectional.” In Journal of Statistical Software 89(4), 2019.
ieee: G. Kurz et al., “Directional Statistics and Filtering Using libDirectional,”
in Journal of Statistical Software 89(4), 2019.
mla: Kurz, Gerhard, et al. “Directional Statistics and Filtering Using LibDirectional.”
Journal of Statistical Software 89(4), 2019.
short: 'G. Kurz, I. Gilitschenski, F. Pfaff, L. Drude, U.D. Hanebeck, R. Haeb-Umbach,
R.Y. Siegwart, in: Journal of Statistical Software 89(4), 2019.'
date_created: 2019-07-23T07:44:59Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-14T07:16:05Z
date_updated: 2019-08-14T07:16:05Z
file_id: '12923'
file_name: JournalofStatisticalSoftware_2019_Drude_Paper.pdf
file_size: 1522964
relation: main_file
file_date_updated: 2019-08-14T07:16:05Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: Journal of Statistical Software 89(4)
status: public
title: Directional Statistics and Filtering Using libDirectional
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12882'
abstract:
- lang: eng
text: One of the major challenges in implementing wireless virtualization is the
resource discovery. This is particularly important for the embedding-algorithms
that are used to distribute the tasks to nodes. MARVELO is a prototype framework
for executing different distributed algorithms on the top of a wireless (802.11)
ad-hoc network. The aim of MARVELO is to select the nodes for running the algorithms
and to define the routing between the nodes. Hence, it also supports monitoring
functionalities to collect information about the available resources and to assist
in profiling the algorithms. The objective of this demo is to show how MAVRLEO
distributes tasks in an ad-hoc network, based on a feedback from our monitoring
tool. Additionally, we explain the work-flow, composition and execution of the
framework.
author:
- first_name: Haitham
full_name: Afifi, Haitham
id: '65718'
last_name: Afifi
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Sebastian
full_name: Eikenberg, Sebastian
last_name: Eikenberg
- first_name: Arnold
full_name: Mueller, Arnold
last_name: Mueller
- first_name: Lars
full_name: Gansel, Lars
last_name: Gansel
- first_name: Alexander
full_name: Makejkin, Alexander
last_name: Makejkin
- first_name: Kai
full_name: Hannemann, Kai
last_name: Hannemann
- first_name: Rafael
full_name: Schellenberg, Rafael
last_name: Schellenberg
citation:
ama: 'Afifi H, Karl H, Eikenberg S, et al. A Rapid Prototyping for Wireless Virtual
Network Embedding using MARVELO. In: 2019 IEEE Wireless Communications and
Networking Conference (WCNC) (IEEE WCNC 2019) (Demo). Marrakech, Morocco;
2019.'
apa: Afifi, H., Karl, H., Eikenberg, S., Mueller, A., Gansel, L., Makejkin, A.,
… Schellenberg, R. (2019). A Rapid Prototyping for Wireless Virtual Network Embedding
using MARVELO. In 2019 IEEE Wireless Communications and Networking Conference
(WCNC) (IEEE WCNC 2019) (Demo). Marrakech, Morocco.
bibtex: '@inproceedings{Afifi_Karl_Eikenberg_Mueller_Gansel_Makejkin_Hannemann_Schellenberg_2019,
place={Marrakech, Morocco}, title={A Rapid Prototyping for Wireless Virtual Network
Embedding using MARVELO}, booktitle={2019 IEEE Wireless Communications and Networking
Conference (WCNC) (IEEE WCNC 2019) (Demo)}, author={Afifi, Haitham and Karl, Holger
and Eikenberg, Sebastian and Mueller, Arnold and Gansel, Lars and Makejkin, Alexander
and Hannemann, Kai and Schellenberg, Rafael}, year={2019} }'
chicago: Afifi, Haitham, Holger Karl, Sebastian Eikenberg, Arnold Mueller, Lars
Gansel, Alexander Makejkin, Kai Hannemann, and Rafael Schellenberg. “A Rapid Prototyping
for Wireless Virtual Network Embedding Using MARVELO.” In 2019 IEEE Wireless
Communications and Networking Conference (WCNC) (IEEE WCNC 2019) (Demo). Marrakech,
Morocco, 2019.
ieee: H. Afifi et al., “A Rapid Prototyping for Wireless Virtual Network
Embedding using MARVELO,” in 2019 IEEE Wireless Communications and Networking
Conference (WCNC) (IEEE WCNC 2019) (Demo), 2019.
mla: Afifi, Haitham, et al. “A Rapid Prototyping for Wireless Virtual Network Embedding
Using MARVELO.” 2019 IEEE Wireless Communications and Networking Conference
(WCNC) (IEEE WCNC 2019) (Demo), 2019.
short: 'H. Afifi, H. Karl, S. Eikenberg, A. Mueller, L. Gansel, A. Makejkin, K.
Hannemann, R. Schellenberg, in: 2019 IEEE Wireless Communications and Networking
Conference (WCNC) (IEEE WCNC 2019) (Demo), Marrakech, Morocco, 2019.'
date_created: 2019-07-24T07:28:45Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '006'
department:
- _id: '75'
file:
- access_level: open_access
content_type: application/pdf
creator: hafifi
date_created: 2021-01-30T12:39:43Z
date_updated: 2021-01-30T12:42:31Z
file_id: '21113'
file_name: demo.pdf
file_size: 102976
relation: main_file
file_date_updated: 2021-01-30T12:42:31Z
has_accepted_license: '1'
keyword:
- WSN
- virtualization
- VNE
language:
- iso: eng
oa: '1'
place: Marrakech, Morocco
project:
- _id: '27'
name: 'Akustische Sensornetzwerke - Teilprojekt '
publication: 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE
WCNC 2019) (Demo)
status: public
title: A Rapid Prototyping for Wireless Virtual Network Embedding using MARVELO
type: conference
user_id: '65718'
year: '2019'
...
---
_id: '12890'
abstract:
- lang: eng
text: 'We formulate a generic framework for blind source separation (BSS), which
allows integrating data-driven spectro-temporal methods, such as deep clustering
and deep attractor networks, with physically motivated probabilistic spatial methods,
such as complex angular central Gaussian mixture models. The integrated model
exploits the complementary strengths of the two approaches to BSS: the strong
modeling power of neural networks, which, however, is based on supervised learning,
and the ease of unsupervised learning of the spatial mixture models whose few
parameters can be estimated on as little as a single segment of a real mixture
of speech. Experiments are carried out on both artificially mixed speech and true
recordings of speech mixtures. The experiments verify that the integrated models
consistently outperform the individual components. We further extend the models
to cope with noisy, reverberant speech and introduce a cross-domain teacher–student
training where the mixture model serves as the teacher to provide training targets
for the student neural network.'
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Drude L, Haeb-Umbach R. Integration of Neural Networks and Probabilistic Spatial
Models for Acoustic Blind Source Separation. IEEE Journal of Selected Topics
in Signal Processing. 2019. doi:10.1109/JSTSP.2019.2912565
apa: Drude, L., & Haeb-Umbach, R. (2019). Integration of Neural Networks and
Probabilistic Spatial Models for Acoustic Blind Source Separation. IEEE Journal
of Selected Topics in Signal Processing. https://doi.org/10.1109/JSTSP.2019.2912565
bibtex: '@article{Drude_Haeb-Umbach_2019, title={Integration of Neural Networks
and Probabilistic Spatial Models for Acoustic Blind Source Separation}, DOI={10.1109/JSTSP.2019.2912565},
journal={IEEE Journal of Selected Topics in Signal Processing}, author={Drude,
Lukas and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks
and Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE
Journal of Selected Topics in Signal Processing, 2019. https://doi.org/10.1109/JSTSP.2019.2912565.
ieee: L. Drude and R. Haeb-Umbach, “Integration of Neural Networks and Probabilistic
Spatial Models for Acoustic Blind Source Separation,” IEEE Journal of Selected
Topics in Signal Processing, 2019.
mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Integration of Neural Networks and
Probabilistic Spatial Models for Acoustic Blind Source Separation.” IEEE Journal
of Selected Topics in Signal Processing, 2019, doi:10.1109/JSTSP.2019.2912565.
short: L. Drude, R. Haeb-Umbach, IEEE Journal of Selected Topics in Signal Processing
(2019).
date_created: 2019-07-26T08:38:46Z
date_updated: 2022-01-06T06:51:23Z
ddc:
- '050'
department:
- _id: '54'
doi: 10.1109/JSTSP.2019.2912565
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-07T07:12:21Z
date_updated: 2019-08-14T07:11:22Z
file_id: '12903'
file_name: IEEE Jounal_2019_Drude_Paper.pdf
file_size: 967424
relation: main_file
file_date_updated: 2019-08-14T07:11:22Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Journal of Selected Topics in Signal Processing
publication_identifier:
eissn:
- 1941-0484
status: public
title: Integration of Neural Networks and Probabilistic Spatial Models for Acoustic
Blind Source Separation
type: journal_article
user_id: '11213'
year: '2019'
...