---
_id: '12900'
abstract:
- lang: eng
text: 'Deep attractor networks (DANs) are a recently introduced method to blindly
separate sources from spectral features of a monaural recording using bidirectional
long short-term memory networks (BLSTMs). Due to the nature of BLSTMs, this is
inherently not online-ready and resorting to operating on blocks yields a block
permutation problem in that the index of each speaker may change between blocks.
We here propose the joint modeling of spatial and spectral features to solve the
block permutation problem and generalize DANs to multi-channel meeting recordings:
The DAN acts as a spectral feature extractor for a subsequent model-based clustering
approach. We first analyze different joint models in batch-processing scenarios
and finally propose a block-online blind source separation algorithm. The efficacy
of the proposed models is demonstrated on reverberant mixtures corrupted by real
recordings of multi-channel background noise. We demonstrate that both the proposed
batch-processing and the proposed block-online system outperform (a) a spatial-only
model with a state-of-the-art frequency permutation solver and (b) a spectral-only
model with an oracle block permutation solver in terms of signal to distortion
ratio (SDR) gains.'
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: ' Takuya '
full_name: 'Higuchi,, Takuya '
last_name: Higuchi,
- first_name: 'Keisuke '
full_name: 'Kinoshita, Keisuke '
last_name: Kinoshita
- first_name: 'Tomohiro '
full_name: 'Nakatani, Tomohiro '
last_name: Nakatani
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Higuchi, Takuya , Kinoshita K, Nakatani T, Haeb-Umbach R. Dual Frequency-
and Block-Permutation Alignment for Deep Learning Based Block-Online Blind Source
Separation. In: ICASSP 2018, Calgary, Canada. ; 2018.'
apa: Drude, L., Higuchi, Takuya , Kinoshita, K., Nakatani, T., & Haeb-Umbach,
R. (2018). Dual Frequency- and Block-Permutation Alignment for Deep Learning Based
Block-Online Blind Source Separation. In ICASSP 2018, Calgary, Canada.
bibtex: '@inproceedings{Drude_Higuchi,_Kinoshita_Nakatani_Haeb-Umbach_2018, title={Dual
Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation}, booktitle={ICASSP 2018, Calgary, Canada}, author={Drude,
Lukas and Higuchi, Takuya and Kinoshita, Keisuke and Nakatani, Tomohiro and
Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Drude, Lukas, Takuya Higuchi, Keisuke Kinoshita, Tomohiro Nakatani,
and Reinhold Haeb-Umbach. “Dual Frequency- and Block-Permutation Alignment for
Deep Learning Based Block-Online Blind Source Separation.” In ICASSP 2018,
Calgary, Canada, 2018.
ieee: L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, and R. Haeb-Umbach,
“Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation,” in ICASSP 2018, Calgary, Canada, 2018.
mla: Drude, Lukas, et al. “Dual Frequency- and Block-Permutation Alignment for Deep
Learning Based Block-Online Blind Source Separation.” ICASSP 2018, Calgary,
Canada, 2018.
short: 'L. Drude, Takuya Higuchi, K. Kinoshita, T. Nakatani, R. Haeb-Umbach, in:
ICASSP 2018, Calgary, Canada, 2018.'
date_created: 2019-07-30T14:42:15Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Drude_Poster.pdf
status: public
title: Dual Frequency- and Block-Permutation Alignment for Deep Learning Based Block-Online
Blind Source Separation
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '12901'
abstract:
- lang: eng
text: This work examines acoustic beamformers employing neural networks (NNs) for
mask prediction as front-end for automatic speech recognition (ASR) systems for
practical scenarios like voice-enabled home devices. To test the versatility of
the mask predicting network, the system is evaluated with different recording
hardware, different microphone array designs, and different acoustic models of
the downstream ASR system. Significant gains in recognition accuracy are obtained
in all configurations despite the fact that the NN had been trained on mismatched
data. Unlike previous work, the NN is trained on a feature level objective, which
gives some performance advantage over a mask related criterion. Furthermore, different
approaches for realizing online, or adaptive, NN-based beamforming are explored,
where the online algorithms still show significant gains compared to the baseline
performance.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Hakan
full_name: Erdogan, Hakan
last_name: Erdogan
- first_name: Takuya
full_name: Yoshioka, Takuya
last_name: Yoshioka
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Erdogan H, Yoshioka T, Haeb-Umbach R. Exploring Practical Aspects
of Neural Mask-Based Beamforming for Far-Field Speech Recognition. In: ICASSP
2018, Calgary, Canada. ; 2018.'
apa: Boeddeker, C., Erdogan, H., Yoshioka, T., & Haeb-Umbach, R. (2018). Exploring
Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition.
In ICASSP 2018, Calgary, Canada.
bibtex: '@inproceedings{Boeddeker_Erdogan_Yoshioka_Haeb-Umbach_2018, title={Exploring
Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition},
booktitle={ICASSP 2018, Calgary, Canada}, author={Boeddeker, Christoph and Erdogan,
Hakan and Yoshioka, Takuya and Haeb-Umbach, Reinhold}, year={2018} }'
chicago: Boeddeker, Christoph, Hakan Erdogan, Takuya Yoshioka, and Reinhold Haeb-Umbach.
“Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field Speech
Recognition.” In ICASSP 2018, Calgary, Canada, 2018.
ieee: C. Boeddeker, H. Erdogan, T. Yoshioka, and R. Haeb-Umbach, “Exploring Practical
Aspects of Neural Mask-Based Beamforming for Far-Field Speech Recognition,” in
ICASSP 2018, Calgary, Canada, 2018.
mla: Boeddeker, Christoph, et al. “Exploring Practical Aspects of Neural Mask-Based
Beamforming for Far-Field Speech Recognition.” ICASSP 2018, Calgary, Canada,
2018.
short: 'C. Boeddeker, H. Erdogan, T. Yoshioka, R. Haeb-Umbach, in: ICASSP 2018,
Calgary, Canada, 2018.'
date_created: 2019-07-30T14:53:58Z
date_updated: 2022-01-06T06:51:24Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Boeddeker_Paper.pdf
oa: '1'
publication: ICASSP 2018, Calgary, Canada
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2018/ICASSP_2018_Boeddeker_Slides.pdf
status: public
title: Exploring Practical Aspects of Neural Mask-Based Beamforming for Far-Field
Speech Recognition
type: conference
user_id: '44006'
year: '2018'
...
---
_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: '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: '11717'
abstract:
- lang: eng
text: In this work, we address the limited availability of large annotated databases
for real-life audio event detection by utilizing the concept of transfer learning.
This technique aims to transfer knowledge from a source domain to a target domain,
even if source and target have different feature distributions and label sets.
We hypothesize that all acoustic events share the same inventory of basic acoustic
building blocks and differ only in the temporal order of these acoustic units.
We then construct a deep neural network with convolutional layers for extracting
the acoustic units and a recurrent layer for capturing the temporal order. Under
the above hypothesis, transfer learning from a source to a target domain with
a different acoustic event inventory is realized by transferring the convolutional
layers from the source to the target domain. The recurrent layer is, however,
learnt directly from the target domain. Experiments on the transfer from a synthetic
source database to the reallife target database of DCASE 2016 demonstrate that
transfer learning leads to improved detection performance on average. However,
the successful transfer to detect events which are very different from what was
seen in the source domain, could not be verified.
author:
- first_name: Prerna
full_name: Arora, Prerna
last_name: Arora
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Arora P, Haeb-Umbach R. A Study on Transfer Learning for Acoustic Event Detection
in a Real Life Scenario. In: IEEE 19th International Workshop on Multimedia
Signal Processing (MMSP). ; 2017.'
apa: Arora, P., & Haeb-Umbach, R. (2017). A Study on Transfer Learning for Acoustic
Event Detection in a Real Life Scenario. In IEEE 19th International Workshop
on Multimedia Signal Processing (MMSP).
bibtex: '@inproceedings{Arora_Haeb-Umbach_2017, title={A Study on Transfer Learning
for Acoustic Event Detection in a Real Life Scenario}, booktitle={IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP)}, author={Arora, Prerna and Haeb-Umbach,
Reinhold}, year={2017} }'
chicago: Arora, Prerna, and Reinhold Haeb-Umbach. “A Study on Transfer Learning
for Acoustic Event Detection in a Real Life Scenario.” In IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP), 2017.
ieee: P. Arora and R. Haeb-Umbach, “A Study on Transfer Learning for Acoustic Event
Detection in a Real Life Scenario,” in IEEE 19th International Workshop on
Multimedia Signal Processing (MMSP), 2017.
mla: Arora, Prerna, and Reinhold Haeb-Umbach. “A Study on Transfer Learning for
Acoustic Event Detection in a Real Life Scenario.” IEEE 19th International
Workshop on Multimedia Signal Processing (MMSP), 2017.
short: 'P. Arora, R. Haeb-Umbach, in: IEEE 19th International Workshop on Multimedia
Signal Processing (MMSP), 2017.'
date_created: 2019-07-12T05:26:54Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_AroraHaeb.pdf
oa: '1'
publication: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_AroraHaeb_poster.pdf
status: public
title: A Study on Transfer Learning for Acoustic Event Detection in a Real Life Scenario
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11735'
abstract:
- lang: eng
text: This report describes the computation of gradients by algorithmic differentiation
for statistically optimum beamforming operations. Especially the derivation of
complex-valued functions is a key component of this approach. Therefore the real-valued
algorithmic differentiation is extended via the complex-valued chain rule. In
addition to the basic mathematic operations the derivative of the eigenvalue problem
with complex-valued eigenvectors is one of the key results of this report. The
potential of this approach is shown with experimental results on the CHiME-3 challenge
database. There, the beamforming task is used as a front-end for an ASR system.
With the developed derivatives a joint optimization of a speech enhancement and
speech recognition system w.r.t. the recognition optimization criterion is possible.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- 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: Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. On the Computation
of Complex-Valued Gradients with Application to Statistically Optimum Beamforming.;
2017.
apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R.
(2017). On the Computation of Complex-valued Gradients with Application to
Statistically Optimum Beamforming.
bibtex: '@book{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={On the
Computation of Complex-valued Gradients with Application to Statistically Optimum
Beamforming}, author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas
and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and
Reinhold Haeb-Umbach. On the Computation of Complex-Valued Gradients with Application
to Statistically Optimum Beamforming, 2017.
ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, On
the Computation of Complex-valued Gradients with Application to Statistically
Optimum Beamforming. 2017.
mla: Boeddeker, Christoph, et al. On the Computation of Complex-Valued Gradients
with Application to Statistically Optimum Beamforming. 2017.
short: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, On the
Computation of Complex-Valued Gradients with Application to Statistically Optimum
Beamforming, 2017.
date_created: 2019-07-12T05:27:15Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/ArXiv_2017_BoeddekerHanebrinkHaeb_Article.pdf
oa: '1'
status: public
title: On the Computation of Complex-valued Gradients with Application to Statistically
Optimum Beamforming
type: report
user_id: '40767'
year: '2017'
...
---
_id: '11736'
abstract:
- lang: eng
text: In this paper we show how a neural network for spectral mask estimation for
an acoustic beamformer can be optimized by algorithmic differentiation. Using
the beamformer output SNR as the objective function to maximize, the gradient
is propagated through the beamformer all the way to the neural network which provides
the clean speech and noise masks from which the beamformer coefficients are estimated
by eigenvalue decomposition. A key theoretical result is the derivative of an
eigenvalue problem involving complex-valued eigenvectors. Experimental results
on the CHiME-3 challenge database demonstrate the effectiveness of the approach.
The tools developed in this paper are a key component for an end-to-end optimization
of speech enhancement and speech recognition.
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- 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: 'Boeddeker C, Hanebrink P, Drude L, Heymann J, Haeb-Umbach R. Optimizing Neural-Network
Supported Acoustic Beamforming by Algorithmic Differentiation. In: Proc. IEEE
Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2017.'
apa: Boeddeker, C., Hanebrink, P., Drude, L., Heymann, J., & Haeb-Umbach, R.
(2017). Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic
Differentiation. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal
Processing (ICASSP).
bibtex: '@inproceedings{Boeddeker_Hanebrink_Drude_Heymann_Haeb-Umbach_2017, title={Optimizing
Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation},
booktitle={Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)},
author={Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann,
Jahn and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Boeddeker, Christoph, Patrick Hanebrink, Lukas Drude, Jahn Heymann, and
Reinhold Haeb-Umbach. “Optimizing Neural-Network Supported Acoustic Beamforming
by Algorithmic Differentiation.” In Proc. IEEE Intl. Conf. on Acoustics, Speech
and Signal Processing (ICASSP), 2017.
ieee: C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, and R. Haeb-Umbach, “Optimizing
Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation,”
in Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP),
2017.
mla: Boeddeker, Christoph, et al. “Optimizing Neural-Network Supported Acoustic
Beamforming by Algorithmic Differentiation.” Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2017.
short: 'C. Boeddeker, P. Hanebrink, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc.
IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.'
date_created: 2019-07-12T05:27:16Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_boeddeker_paper.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
status: public
title: Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11737'
abstract:
- lang: eng
text: The benefits of both a logarithmic spectral amplitude (LSA) estimation and
a modeling in a generalized spectral domain (where short-time amplitudes are raised
to a generalized power exponent, not restricted to magnitude or power spectrum)
are combined in this contribution to achieve a better tradeoff between speech
quality and noise suppression in single-channel speech enhancement. A novel gain
function is derived to enhance the logarithmic generalized spectral amplitudes
of noisy speech. Experiments on the CHiME-3 dataset show that it outperforms the
famous minimum mean squared error (MMSE) LSA gain function of Ephraim and Malah
in terms of noise suppression by 1.4 dB, while the good speech quality of the
MMSE-LSA estimator is maintained.
author:
- first_name: Alleksej
full_name: Chinaev, Alleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. A Generalized Log-Spectral Amplitude Estimator for
Single-Channel Speech Enhancement. In: Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP). ; 2017.'
apa: Chinaev, A., & Haeb-Umbach, R. (2017). A Generalized Log-Spectral Amplitude
Estimator for Single-Channel Speech Enhancement. In Proc. IEEE Intl. Conf.
on Acoustics, Speech and Signal Processing (ICASSP).
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2017, title={A Generalized Log-Spectral
Amplitude Estimator for Single-Channel Speech Enhancement}, booktitle={Proc. IEEE
Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Chinaev,
Alleksej and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Chinaev, Alleksej, and Reinhold Haeb-Umbach. “A Generalized Log-Spectral
Amplitude Estimator for Single-Channel Speech Enhancement.” In Proc. IEEE Intl.
Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.
ieee: A. Chinaev and R. Haeb-Umbach, “A Generalized Log-Spectral Amplitude Estimator
for Single-Channel Speech Enhancement,” in Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2017.
mla: Chinaev, Alleksej, and Reinhold Haeb-Umbach. “A Generalized Log-Spectral Amplitude
Estimator for Single-Channel Speech Enhancement.” Proc. IEEE Intl. Conf. on
Acoustics, Speech and Signal Processing (ICASSP), 2017.
short: 'A. Chinaev, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics, Speech
and Signal Processing (ICASSP), 2017.'
date_created: 2019-07-12T05:27:17Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/ChinHaeb17.pdf
oa: '1'
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/ChinHaeb17_Slides.pdf
status: public
title: A Generalized Log-Spectral Amplitude Estimator for Single-Channel Speech Enhancement
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11754'
abstract:
- lang: eng
text: Recent advances in discriminatively trained mask estimation networks to extract
a single source utilizing beamforming techniques demonstrate, that the integration
of statistical models and deep neural networks (DNNs) are a promising approach
for robust automatic speech recognition (ASR) applications. In this contribution
we demonstrate how discriminatively trained embeddings on spectral features can
be tightly integrated into statistical model-based source separation to separate
and transcribe overlapping speech. Good generalization to unseen spatial configurations
is achieved by estimating a statistical model at test time, while still leveraging
discriminative training of deep clustering embeddings on a separate training set.
We formulate an expectation maximization (EM) algorithm which jointly estimates
a model for deep clustering embeddings and complex-valued spatial observations
in the short time Fourier transform (STFT) domain at test time. Extensive simulations
confirm, that the integrated model outperforms (a) a deep clustering model with
a subsequent beamforming step and (b) an EM-based model with a beamforming step
alone in terms of signal to distortion ratio (SDR) and perceptually motivated
metric (PESQ) gains. ASR results on a reverberated dataset further show, that
the aforementioned gains translate to reduced word error rates (WERs) even in
reverberant environments.
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. Tight integration of spatial and spectral features
for BSS with Deep Clustering embeddings. In: INTERSPEECH 2017, Stockholm, Schweden.
; 2017.'
apa: Drude, L., & Haeb-Umbach, R. (2017). Tight integration of spatial and spectral
features for BSS with Deep Clustering embeddings. In INTERSPEECH 2017, Stockholm,
Schweden.
bibtex: '@inproceedings{Drude_Haeb-Umbach_2017, title={Tight integration of spatial
and spectral features for BSS with Deep Clustering embeddings}, booktitle={INTERSPEECH
2017, Stockholm, Schweden}, author={Drude, Lukas and Haeb-Umbach, Reinhold}, year={2017}
}'
chicago: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and
Spectral Features for BSS with Deep Clustering Embeddings.” In INTERSPEECH
2017, Stockholm, Schweden, 2017.
ieee: L. Drude and R. Haeb-Umbach, “Tight integration of spatial and spectral features
for BSS with Deep Clustering embeddings,” in INTERSPEECH 2017, Stockholm, Schweden,
2017.
mla: Drude, Lukas, and Reinhold Haeb-Umbach. “Tight Integration of Spatial and Spectral
Features for BSS with Deep Clustering Embeddings.” INTERSPEECH 2017, Stockholm,
Schweden, 2017.
short: 'L. Drude, R. Haeb-Umbach, in: INTERSPEECH 2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:37Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Drude_slides.pdf
status: public
title: Tight integration of spatial and spectral features for BSS with Deep Clustering
embeddings
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11770'
abstract:
- lang: eng
text: 'In this contribution we show how to exploit text data to support word discovery
from audio input in an underresourced target language. Given audio, of which a
certain amount is transcribed at the word level, and additional unrelated text
data, the approach is able to learn a probabilistic mapping from acoustic units
to characters and utilize it to segment the audio data into words without the
need of a pronunciation dictionary. This is achieved by three components: an unsupervised
acoustic unit discovery system, a supervisedly trained acoustic unit-to-grapheme
converter, and a word discovery system, which is initialized with a language model
trained on the text data. Experiments for multiple setups show that the initialization
of the language model with text data improves the word segementation performance
by a large margin.'
author:
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Benedikt
full_name: Boenninghoff, Benedikt
last_name: Boenninghoff
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Glarner T, Boenninghoff B, Walter O, Haeb-Umbach R. Leveraging Text Data for
Word Segmentation for Underresourced Languages. In: INTERSPEECH 2017, Stockholm,
Schweden. ; 2017.'
apa: Glarner, T., Boenninghoff, B., Walter, O., & Haeb-Umbach, R. (2017). Leveraging
Text Data for Word Segmentation for Underresourced Languages. In INTERSPEECH
2017, Stockholm, Schweden.
bibtex: '@inproceedings{Glarner_Boenninghoff_Walter_Haeb-Umbach_2017, title={Leveraging
Text Data for Word Segmentation for Underresourced Languages}, booktitle={INTERSPEECH
2017, Stockholm, Schweden}, author={Glarner, Thomas and Boenninghoff, Benedikt
and Walter, Oliver and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Glarner, Thomas, Benedikt Boenninghoff, Oliver Walter, and Reinhold Haeb-Umbach.
“Leveraging Text Data for Word Segmentation for Underresourced Languages.” In
INTERSPEECH 2017, Stockholm, Schweden, 2017.
ieee: T. Glarner, B. Boenninghoff, O. Walter, and R. Haeb-Umbach, “Leveraging Text
Data for Word Segmentation for Underresourced Languages,” in INTERSPEECH 2017,
Stockholm, Schweden, 2017.
mla: Glarner, Thomas, et al. “Leveraging Text Data for Word Segmentation for Underresourced
Languages.” INTERSPEECH 2017, Stockholm, Schweden, 2017.
short: 'T. Glarner, B. Boenninghoff, O. Walter, R. Haeb-Umbach, in: INTERSPEECH
2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:55Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Glarner_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Glarner_poster.pdf
status: public
title: Leveraging Text Data for Word Segmentation for Underresourced Languages
type: conference
user_id: '44006'
year: '2017'
...
---
_id: '11809'
abstract:
- lang: eng
text: This paper presents an end-to-end training approach for a beamformer-supported
multi-channel ASR system. A neural network which estimates masks for a statistically
optimum beamformer is jointly trained with a network for acoustic modeling. To
update its parameters, we propagate the gradients from the acoustic model all
the way through feature extraction and the complex valued beamforming operation.
Besides avoiding a mismatch between the front-end and the back-end, this approach
also eliminates the need for stereo data, i.e., the parallel availability of clean
and noisy versions of the signals. Instead, it can be trained with real noisy
multichannel data only. Also, relying on the signal statistics for beamforming,
the approach makes no assumptions on the configuration of the microphone array.
We further observe a performance gain through joint training in terms of word
error rate in an evaluation of the system on the CHiME 4 dataset.
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: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Patrick
full_name: Hanebrink, Patrick
last_name: Hanebrink
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heymann J, Drude L, Boeddeker C, Hanebrink P, Haeb-Umbach R. BEAMNET: End-to-End
Training of a Beamformer-Supported Multi-Channel ASR System. In: Proc. IEEE
Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP). ; 2017.'
apa: 'Heymann, J., Drude, L., Boeddeker, C., Hanebrink, P., & Haeb-Umbach, R.
(2017). BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR
System. In Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP).'
bibtex: '@inproceedings{Heymann_Drude_Boeddeker_Hanebrink_Haeb-Umbach_2017, title={BEAMNET:
End-to-End Training of a Beamformer-Supported Multi-Channel ASR System}, booktitle={Proc.
IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Heymann,
Jahn and Drude, Lukas and Boeddeker, Christoph and Hanebrink, Patrick and Haeb-Umbach,
Reinhold}, year={2017} }'
chicago: 'Heymann, Jahn, Lukas Drude, Christoph Boeddeker, Patrick Hanebrink, and
Reinhold Haeb-Umbach. “BEAMNET: End-to-End Training of a Beamformer-Supported
Multi-Channel ASR System.” In Proc. IEEE Intl. Conf. on Acoustics, Speech and
Signal Processing (ICASSP), 2017.'
ieee: 'J. Heymann, L. Drude, C. Boeddeker, P. Hanebrink, and R. Haeb-Umbach, “BEAMNET:
End-to-End Training of a Beamformer-Supported Multi-Channel ASR System,” in Proc.
IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.'
mla: 'Heymann, Jahn, et al. “BEAMNET: End-to-End Training of a Beamformer-Supported
Multi-Channel ASR System.” Proc. IEEE Intl. Conf. on Acoustics, Speech and
Signal Processing (ICASSP), 2017.'
short: 'J. Heymann, L. Drude, C. Boeddeker, P. Hanebrink, R. Haeb-Umbach, in: Proc.
IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2017.'
date_created: 2019-07-12T05:28:40Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_heymann_paper.pdf
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/icassp_2017_heymann_poster.pdf
status: public
title: 'BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System'
type: conference
user_id: '40767'
year: '2017'
...
---
_id: '11811'
abstract:
- lang: eng
text: 'Acoustic beamforming can greatly improve the performance of Automatic Speech
Recognition (ASR) and speech enhancement systems when multiple channels are available.
We recently proposed a way to support the model-based Generalized Eigenvalue beamforming
operation with a powerful neural network for spectral mask estimation. The enhancement
system has a number of desirable properties. In particular, neither assumptions
need to be made about the nature of the acoustic transfer function (e.g., being
anechonic), nor does the array configuration need to be known. While the system
has been originally developed to enhance speech in noisy environments, we show
in this article that it is also effective in suppressing reverberation, thus leading
to a generic trainable multi-channel speech enhancement system for robust speech
processing. To support this claim, we consider two distinct datasets: The CHiME
3 challenge, which features challenging real-world noise distortions, and the
Reverb challenge, which focuses on distortions caused by reverberation. We evaluate
the system both with respect to a speech enhancement and a recognition task. For
the first task we propose a new way to cope with the distortions introduced by
the Generalized Eigenvalue beamformer by renormalizing the target energy for each
frequency bin, and measure its effectiveness in terms of the PESQ score. For the
latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art
ASR results on both datasets. We further experiment with different network architectures
for spectral mask estimation: One small feed-forward network with only one hidden
layer, one Convolutional Neural Network and one bi-directional Long Short-Term
Memory network, showing that even a small network is capable of delivering significant
performance improvements.'
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
citation:
ama: Heymann J, Drude L, Haeb-Umbach R. A Generic Neural Acoustic Beamforming Architecture
for Robust Multi-Channel Speech Processing. Computer Speech and Language.
2017.
apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2017). A Generic Neural Acoustic
Beamforming Architecture for Robust Multi-Channel Speech Processing. Computer
Speech and Language.
bibtex: '@article{Heymann_Drude_Haeb-Umbach_2017, title={A Generic Neural Acoustic
Beamforming Architecture for Robust Multi-Channel Speech Processing}, journal={Computer
Speech and Language}, author={Heymann, Jahn and Drude, Lukas and Haeb-Umbach,
Reinhold}, year={2017} }'
chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “A Generic Neural
Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing.”
Computer Speech and Language, 2017.
ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “A Generic Neural Acoustic Beamforming
Architecture for Robust Multi-Channel Speech Processing,” Computer Speech and
Language, 2017.
mla: Heymann, Jahn, et al. “A Generic Neural Acoustic Beamforming Architecture for
Robust Multi-Channel Speech Processing.” Computer Speech and Language,
2017.
short: J. Heymann, L. Drude, R. Haeb-Umbach, Computer Speech and Language (2017).
date_created: 2019-07-12T05:28:43Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/ComputerSpeechLanguage_2017_heymann_paper.pdf
oa: '1'
publication: Computer Speech and Language
status: public
title: A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel
Speech Processing
type: journal_article
user_id: '44006'
year: '2017'
...
---
_id: '11763'
abstract:
- lang: eng
text: In this paper, we apply a high-resolution approach, i.e. the matrix pencil
method (MPM), to the FMCW automotive radar system to separate the neighboring
targets, which share similar parameters, i.e. range, relative speed and azimuth
angle, and cause overlapping in the radar spectrum. In order to adapt the 1D model
of MPM to the 2D range-velocity spectrum and simultaneously limit the computational
cost, some preprocessing steps are proposed to construct a novel separation algorithm.
Finally, this algorithm is evaluated in both simulation and real data, and the
results indicate a promising performance.
author:
- first_name: Tai
full_name: Fei, Tai
last_name: Fei
- first_name: Christopher
full_name: Grimm, Christopher
last_name: Grimm
- first_name: Ridha
full_name: Farhoud, Ridha
last_name: Farhoud
- first_name: Tobias
full_name: Breddermann, Tobias
last_name: Breddermann
- 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: 'Fei T, Grimm C, Farhoud R, Breddermann T, Warsitz E, Haeb-Umbach R. A Novel
Target Separation Algorithm Applied to The Two-Dimensional Spectrum for FMCW Automotive
Radar Systems. In: IEEE International Conference on Microwave, Communications,
Anthenas and Electronic Systems. ; 2017.'
apa: Fei, T., Grimm, C., Farhoud, R., Breddermann, T., Warsitz, E., & Haeb-Umbach,
R. (2017). A Novel Target Separation Algorithm Applied to The Two-Dimensional
Spectrum for FMCW Automotive Radar Systems. IEEE International Conference on
Microwave, Communications, Anthenas and Electronic Systems.
bibtex: '@inproceedings{Fei_Grimm_Farhoud_Breddermann_Warsitz_Haeb-Umbach_2017,
title={A Novel Target Separation Algorithm Applied to The Two-Dimensional Spectrum
for FMCW Automotive Radar Systems}, booktitle={IEEE International conference on
microwave, communications, anthenas and electronic systems}, author={Fei, Tai
and Grimm, Christopher and Farhoud, Ridha and Breddermann, Tobias and Warsitz,
Ernst and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Fei, Tai, Christopher Grimm, Ridha Farhoud, Tobias Breddermann, Ernst Warsitz,
and Reinhold Haeb-Umbach. “A Novel Target Separation Algorithm Applied to The
Two-Dimensional Spectrum for FMCW Automotive Radar Systems.” In IEEE International
Conference on Microwave, Communications, Anthenas and Electronic Systems,
2017.
ieee: T. Fei, C. Grimm, R. Farhoud, T. Breddermann, E. Warsitz, and R. Haeb-Umbach,
“A Novel Target Separation Algorithm Applied to The Two-Dimensional Spectrum for
FMCW Automotive Radar Systems,” 2017.
mla: Fei, Tai, et al. “A Novel Target Separation Algorithm Applied to The Two-Dimensional
Spectrum for FMCW Automotive Radar Systems.” IEEE International Conference
on Microwave, Communications, Anthenas and Electronic Systems, 2017.
short: 'T. Fei, C. Grimm, R. Farhoud, T. Breddermann, E. Warsitz, R. Haeb-Umbach,
in: IEEE International Conference on Microwave, Communications, Anthenas and Electronic
Systems, 2017.'
date_created: 2019-07-12T05:27:47Z
date_updated: 2023-11-20T16:37:49Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/COMCAS_2017_FeiHaeb_paper.pdf
oa: '1'
publication: IEEE International conference on microwave, communications, anthenas
and electronic systems
quality_controlled: '1'
status: public
title: A Novel Target Separation Algorithm Applied to The Two-Dimensional Spectrum
for FMCW Automotive Radar Systems
type: conference
user_id: '242'
year: '2017'
...
---
_id: '11772'
abstract:
- lang: eng
text: In this paper, we present a hypothesis test for the classification of moving
targets in the sight of an automotive radar sensor. For this purpose, a statistical
model of the relative velocity between a stationary target and the radar sensor
has been developed. With respect to the statistical properties a confidence interval
is calculated and targets with relative velocity lying outside this interval are
classified as moving targets. Compared to existing algorithms our approach is
able to give robust classification independent of the number of observed moving
targets and is characterized by an instantaneous classification, a simple parameterization
of the model and an automatic calculation of the discriminating threshold.
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. Hypothesis
Test for the Detection of Moving Targets in Automotive Radar. In: IEEE International
Conference on Microwave, Communications, Anthenas and Electronic Systems (COMCAS).
; 2017.'
apa: Grimm, C., Breddermann, T., Farhoud, R., Fei, T., Warsitz, E., & Haeb-Umbach,
R. (2017). Hypothesis Test for the Detection of Moving Targets in Automotive Radar.
IEEE International Conference on Microwave, Communications, Anthenas and Electronic
Systems (COMCAS).
bibtex: '@inproceedings{Grimm_Breddermann_Farhoud_Fei_Warsitz_Haeb-Umbach_2017,
title={Hypothesis Test for the Detection of Moving Targets in Automotive Radar},
booktitle={IEEE International conference on microwave, communications, anthenas
and electronic systems (COMCAS)}, author={Grimm, Christopher and Breddermann,
Tobias and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach, Reinhold},
year={2017} }'
chicago: Grimm, Christopher, Tobias Breddermann, Ridha Farhoud, Tai Fei, Ernst Warsitz,
and Reinhold Haeb-Umbach. “Hypothesis Test for the Detection of Moving Targets
in Automotive Radar.” In IEEE International Conference on Microwave, Communications,
Anthenas and Electronic Systems (COMCAS), 2017.
ieee: C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach,
“Hypothesis Test for the Detection of Moving Targets in Automotive Radar,” 2017.
mla: Grimm, Christopher, et al. “Hypothesis Test for the Detection of Moving Targets
in Automotive Radar.” IEEE International Conference on Microwave, Communications,
Anthenas and Electronic Systems (COMCAS), 2017.
short: 'C. Grimm, T. Breddermann, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach,
in: IEEE International Conference on Microwave, Communications, Anthenas and Electronic
Systems (COMCAS), 2017.'
date_created: 2019-07-12T05:27:57Z
date_updated: 2023-11-20T16:37:59Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/COMCAS_2017_GrimmHaeb_paper.pdf
oa: '1'
publication: IEEE International conference on microwave, communications, anthenas
and electronic systems (COMCAS)
quality_controlled: '1'
status: public
title: Hypothesis Test for the Detection of Moving Targets in Automotive Radar
type: conference
user_id: '242'
year: '2017'
...