---
_id: '15952'
abstract:
- lang: eng
text: Arbitrary sampling rate conversion has already received considerable attention
in the past, but still lacks an equivalent representation of the effective time-dilation
process in the block frequency domain. Good sampling rate converters in the time
domain have been known, for instance, in terms of time-varying 'Sinc' or fixed
'Farrow' polynomial filters. The former can deliver nearly exact conversion at
high complexity, while the latter has pronounced computational efficiency with
limited accuracy. Only recently, it was shown that a composite 'polyphase Farrow'
form with high resampling precision can be implemented with quasi-fixed filters
that operate at the input sampling rate. We therefore propose to capitalize from
that fixed-filter architecture in that we translate the polyphase-Farrow filters
into an equivalent FFT-based overlap-save form. Experimental evaluation and comparison
with other state-of-the art frequency-domain approaches then proves currently
the best price-performance ratio of the proposed algorithm. It is thus an ideal
candidate for the new framework of acoustic sensor networks that critically rests
upon fast and accurate alignment of autonomous sampling processes.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Gerald
full_name: Enzner, Gerald
last_name: Enzner
citation:
ama: 'Schmalenstroeer J, Chinaev A, Enzner G. Fast and Accurate Audio Resampling
for Acoustic Sensor Networks by Polyphase-Farrow Filters with FFT Realization.
In: Speech Communication; 13th ITG-Symposium. ; 2018:1-5.'
apa: Schmalenstroeer, J., Chinaev, A., & Enzner, G. (2018). Fast and Accurate
Audio Resampling for Acoustic Sensor Networks by Polyphase-Farrow Filters with
FFT Realization. Speech Communication; 13th ITG-Symposium, 1–5.
bibtex: '@inproceedings{Schmalenstroeer_Chinaev_Enzner_2018, title={Fast and Accurate
Audio Resampling for Acoustic Sensor Networks by Polyphase-Farrow Filters with
FFT Realization}, booktitle={Speech Communication; 13th ITG-Symposium}, author={Schmalenstroeer,
Joerg and Chinaev, Aleksej and Enzner, Gerald}, year={2018}, pages={1–5} }'
chicago: Schmalenstroeer, Joerg, Aleksej Chinaev, and Gerald Enzner. “Fast and Accurate
Audio Resampling for Acoustic Sensor Networks by Polyphase-Farrow Filters with
FFT Realization.” In Speech Communication; 13th ITG-Symposium, 1–5, 2018.
ieee: J. Schmalenstroeer, A. Chinaev, and G. Enzner, “Fast and Accurate Audio Resampling
for Acoustic Sensor Networks by Polyphase-Farrow Filters with FFT Realization,”
in Speech Communication; 13th ITG-Symposium, 2018, pp. 1–5.
mla: Schmalenstroeer, Joerg, et al. “Fast and Accurate Audio Resampling for Acoustic
Sensor Networks by Polyphase-Farrow Filters with FFT Realization.” Speech Communication;
13th ITG-Symposium, 2018, pp. 1–5.
short: 'J. Schmalenstroeer, A. Chinaev, G. Enzner, in: Speech Communication; 13th
ITG-Symposium, 2018, pp. 1–5.'
date_created: 2020-02-21T08:53:14Z
date_updated: 2024-11-14T09:42:35Z
department:
- _id: '54'
language:
- iso: eng
page: 1-5
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Speech Communication; 13th ITG-Symposium
publication_identifier:
issn:
- 'null'
quality_controlled: '1'
status: public
title: Fast and Accurate Audio Resampling for Acoustic Sensor Networks by Polyphase-Farrow
Filters with FFT Realization
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: '12081'
abstract:
- lang: eng
text: 'The invention relates to a building or enclosure termination opening and/or
closing apparatus having communication signed or encrypted by means of a key,
and to a method for operating such. To allow simple, convenient and secure use
by exclusively authorised users, the apparatus comprises: a first and a second
user terminal, with secure forwarding of a time-limited key from the first to
the second user terminal being possible. According to an alternative, individual
keys are generated by a user identification and a secret device key.'
author:
- first_name: Florian
full_name: Jacob, Florian
last_name: Jacob
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
citation:
ama: Jacob F, Schmalenstroeer J. Building or Enclosure Termination Closing and/or
Opening Apparatus, and Method for Operating a Building or Enclosure Termination.
2017.
apa: Jacob, F., & Schmalenstroeer, J. (2017). Building or Enclosure Termination
Closing and/or Opening Apparatus, and Method for Operating a Building or Enclosure
Termination.
bibtex: '@article{Jacob_Schmalenstroeer_2017, title={Building or Enclosure Termination
Closing and/or Opening Apparatus, and Method for Operating a Building or Enclosure
Termination}, author={Jacob, Florian and Schmalenstroeer, Joerg}, year={2017}
}'
chicago: Jacob, Florian, and Joerg Schmalenstroeer. “Building or Enclosure Termination
Closing and/or Opening Apparatus, and Method for Operating a Building or Enclosure
Termination,” 2017.
ieee: F. Jacob and J. Schmalenstroeer, “Building or Enclosure Termination Closing
and/or Opening Apparatus, and Method for Operating a Building or Enclosure Termination.”
2017.
mla: Jacob, Florian, and Joerg Schmalenstroeer. Building or Enclosure Termination
Closing and/or Opening Apparatus, and Method for Operating a Building or Enclosure
Termination. 2017.
short: F. Jacob, J. Schmalenstroeer, (2017).
date_created: 2019-07-19T08:07:11Z
date_updated: 2022-01-06T06:51:17Z
department:
- _id: '54'
ipc: WO2018/077610A
ipn: WO2018/077610A
publication_date: '2017'
status: public
title: Building or Enclosure Termination Closing and/or Opening Apparatus, and Method
for Operating a Building or Enclosure Termination
type: patent
user_id: '460'
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'
...
---
_id: '11759'
abstract:
- lang: eng
text: 'Variational Autoencoders (VAEs) have been shown to provide efficient neural-network-based
approximate Bayesian inference for observation models for which exact inference
is intractable. Its extension, the so-called Structured VAE (SVAE) allows inference
in the presence of both discrete and continuous latent variables. Inspired by
this extension, we developed a VAE with Hidden Markov Models (HMMs) as latent
models. We applied the resulting HMM-VAE to the task of acoustic unit discovery
in a zero resource scenario. Starting from an initial model based on variational
inference in an HMM with Gaussian Mixture Model (GMM) emission probabilities,
the accuracy of the acoustic unit discovery could be significantly improved by
the HMM-VAE. In doing so we were able to demonstrate for an unsupervised learning
task what is well-known in the supervised learning case: Neural networks provide
superior modeling power compared to GMMs.'
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- 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: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
citation:
ama: 'Ebbers J, Heymann J, Drude L, Glarner T, Haeb-Umbach R, Raj B. Hidden Markov
Model Variational Autoencoder for Acoustic Unit Discovery. In: INTERSPEECH
2017, Stockholm, Schweden. ; 2017.'
apa: Ebbers, J., Heymann, J., Drude, L., Glarner, T., Haeb-Umbach, R., & Raj,
B. (2017). Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery.
INTERSPEECH 2017, Stockholm, Schweden.
bibtex: '@inproceedings{Ebbers_Heymann_Drude_Glarner_Haeb-Umbach_Raj_2017, title={Hidden
Markov Model Variational Autoencoder for Acoustic Unit Discovery}, booktitle={INTERSPEECH
2017, Stockholm, Schweden}, author={Ebbers, Janek and Heymann, Jahn and Drude,
Lukas and Glarner, Thomas and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2017}
}'
chicago: Ebbers, Janek, Jahn Heymann, Lukas Drude, Thomas Glarner, Reinhold Haeb-Umbach,
and Bhiksha Raj. “Hidden Markov Model Variational Autoencoder for Acoustic Unit
Discovery.” In INTERSPEECH 2017, Stockholm, Schweden, 2017.
ieee: J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, and B. Raj, “Hidden
Markov Model Variational Autoencoder for Acoustic Unit Discovery,” 2017.
mla: Ebbers, Janek, et al. “Hidden Markov Model Variational Autoencoder for Acoustic
Unit Discovery.” INTERSPEECH 2017, Stockholm, Schweden, 2017.
short: 'J. Ebbers, J. Heymann, L. Drude, T. Glarner, R. Haeb-Umbach, B. Raj, in:
INTERSPEECH 2017, Stockholm, Schweden, 2017.'
date_created: 2019-07-12T05:27:42Z
date_updated: 2023-11-22T08:29:06Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_paper.pdf
oa: '1'
publication: INTERSPEECH 2017, Stockholm, Schweden
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_poster.pdf
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/INTERSPEECH_2017_Ebbers_slides.pdf
status: public
title: Hidden Markov Model Variational Autoencoder for Acoustic Unit Discovery
type: conference
user_id: '34851'
year: '2017'
...
---
_id: '11895'
abstract:
- lang: eng
text: Multi-channel speech enhancement algorithms rely on a synchronous sampling
of the microphone signals. This, however, cannot always be guaranteed, especially
if the sensors are distributed in an environment. To avoid performance degradation
the sampling rate offset needs to be estimated and compensated for. In this contribution
we extend the recently proposed coherence drift based method in two important
directions. First, the increasing phase shift in the short-time Fourier transform
domain is estimated from the coherence drift in a Matched Filterlike fashion,
where intermediate estimates are weighted by their instantaneous SNR. Second,
an observed bias is removed by iterating between offset estimation and compensation
by resampling a couple of times. The effectiveness of the proposed method is demonstrated
by speech recognition results on the output of a beamformer with and without sampling
rate offset compensation between the input channels. We compare MVDR and maximum-SNR
beamformers in reverberant environments and further show that both benefit from
a novel phase normalization, which we also propose in this contribution.
author:
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- 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: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Schmalenstroeer J, Heymann J, Drude L, Boeddeker C, Haeb-Umbach R. Multi-Stage
Coherence Drift Based Sampling Rate Synchronization for Acoustic Beamforming.
In: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP).
; 2017.'
apa: Schmalenstroeer, J., Heymann, J., Drude, L., Boeddeker, C., & Haeb-Umbach,
R. (2017). Multi-Stage Coherence Drift Based Sampling Rate Synchronization for
Acoustic Beamforming. IEEE 19th International Workshop on Multimedia Signal
Processing (MMSP).
bibtex: '@inproceedings{Schmalenstroeer_Heymann_Drude_Boeddeker_Haeb-Umbach_2017,
title={Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
Beamforming}, booktitle={IEEE 19th International Workshop on Multimedia Signal
Processing (MMSP)}, author={Schmalenstroeer, Joerg and Heymann, Jahn and Drude,
Lukas and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2017} }'
chicago: Schmalenstroeer, Joerg, Jahn Heymann, Lukas Drude, Christoph Boeddeker,
and Reinhold Haeb-Umbach. “Multi-Stage Coherence Drift Based Sampling Rate Synchronization
for Acoustic Beamforming.” In IEEE 19th International Workshop on Multimedia
Signal Processing (MMSP), 2017.
ieee: J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, and R. Haeb-Umbach,
“Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
Beamforming,” 2017.
mla: Schmalenstroeer, Joerg, et al. “Multi-Stage Coherence Drift Based Sampling
Rate Synchronization for Acoustic Beamforming.” IEEE 19th International Workshop
on Multimedia Signal Processing (MMSP), 2017.
short: 'J. Schmalenstroeer, J. Heymann, L. Drude, C. Boeddeker, R. Haeb-Umbach,
in: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP), 2017.'
date_created: 2019-07-12T05:30:20Z
date_updated: 2023-10-26T08:12:05Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb.pdf
oa: '1'
publication: IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)
quality_controlled: '1'
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2017/MMSP_2017_SchHaeb_poster.pdf
status: public
title: Multi-Stage Coherence Drift Based Sampling Rate Synchronization for Acoustic
Beamforming
type: conference
user_id: '460'
year: '2017'
...
---
_id: '11773'
abstract:
- lang: eng
text: In this paper we present an algorithm for the detection of moving targets
in sight of an automotive radar sensor which can handle distorted ego-velocity
information. In situations where biased or none velocity information is provided
from the ego-vehicle, the algorithm is able to estimate the ego-velocity based
on previously detected stationary targets with high accuracy, subsequently used
for the target classification. Compared to existing ego-velocity algorithms our
approach provides fast and efficient inference without sacrificing the practical
classification accuracy. Other than that the algorithm is characterized by simple
parameterization and little but appropriate model assumptions for high accurate
production automotive radar sensors.
author:
- first_name: Christopher
full_name: Grimm, Christopher
last_name: Grimm
- 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, Farhoud R, Fei T, Warsitz E, Haeb-Umbach R. Detection of Moving Targets
in Automotive Radar with Distorted Ego-Velocity Information. In: IEEE Microwaves,
Radar and Remote Sensing Symposium (MRRS). ; 2017.'
apa: Grimm, C., Farhoud, R., Fei, T., Warsitz, E., & Haeb-Umbach, R. (2017).
Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity Information.
IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS).
bibtex: '@inproceedings{Grimm_Farhoud_Fei_Warsitz_Haeb-Umbach_2017, title={Detection
of Moving Targets in Automotive Radar with Distorted Ego-Velocity Information},
booktitle={IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)}, author={Grimm,
Christopher and Farhoud, Ridha and Fei, Tai and Warsitz, Ernst and Haeb-Umbach,
Reinhold}, year={2017} }'
chicago: Grimm, Christopher, Ridha Farhoud, Tai Fei, Ernst Warsitz, and Reinhold
Haeb-Umbach. “Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity
Information.” In IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS),
2017.
ieee: C. Grimm, R. Farhoud, T. Fei, E. Warsitz, and R. Haeb-Umbach, “Detection of
Moving Targets in Automotive Radar with Distorted Ego-Velocity Information,” 2017.
mla: Grimm, Christopher, et al. “Detection of Moving Targets in Automotive Radar
with Distorted Ego-Velocity Information.” IEEE Microwaves, Radar and Remote
Sensing Symposium (MRRS), 2017.
short: 'C. Grimm, R. Farhoud, T. Fei, E. Warsitz, R. Haeb-Umbach, in: IEEE Microwaves,
Radar and Remote Sensing Symposium (MRRS), 2017.'
date_created: 2019-07-12T05:27:59Z
date_updated: 2023-11-20T16:38:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2017/MRRS_2017_GrimmHaeb_paper.pdf
oa: '1'
publication: IEEE Microwaves, Radar and Remote Sensing Symposium (MRRS)
quality_controlled: '1'
status: public
title: Detection of Moving Targets in Automotive Radar with Distorted Ego-Velocity
Information
type: conference
user_id: '242'
year: '2017'
...
---
_id: '11738'
abstract:
- lang: eng
text: 'In this contribution we investigate a priori signal-to-noise ratio (SNR)
estimation, a crucial component of a single-channel speech enhancement system
based on spectral subtraction. The majority of the state-of-the art a priori SNR
estimators work in the power spectral domain, which is, however, not confirmed
to be the optimal domain for the estimation. Motivated by the generalized spectral
subtraction rule, we show how the estimation of the a priori SNR can be formulated
in the so called generalized SNR domain. This formulation allows to generalize
the widely used decision directed (DD) approach. An experimental investigation
with different noise types reveals the superiority of the generalized DD approach
over the conventional DD approach in terms of both the mean opinion score - listening
quality objective measure and the output global SNR in the medium to high input
SNR regime, while we show that the power spectrum is the optimal domain for low
SNR. We further develop a parameterization which adjusts the domain of estimation
automatically according to the estimated input global SNR. Index Terms: single-channel
speech enhancement, a priori SNR estimation, generalized spectral subtraction'
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
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 Priori SNR Estimation Using a Generalized Decision
Directed Approach. In: INTERSPEECH 2016, San Francisco, USA. ; 2016.'
apa: Chinaev, A., & Haeb-Umbach, R. (2016). A Priori SNR Estimation Using a
Generalized Decision Directed Approach. In INTERSPEECH 2016, San Francisco,
USA.
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2016, title={A Priori SNR Estimation
Using a Generalized Decision Directed Approach}, booktitle={INTERSPEECH 2016,
San Francisco, USA}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2016}
}'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using
a Generalized Decision Directed Approach.” In INTERSPEECH 2016, San Francisco,
USA, 2016.
ieee: A. Chinaev and R. Haeb-Umbach, “A Priori SNR Estimation Using a Generalized
Decision Directed Approach,” in INTERSPEECH 2016, San Francisco, USA, 2016.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “A Priori SNR Estimation Using
a Generalized Decision Directed Approach.” INTERSPEECH 2016, San Francisco,
USA, 2016.
short: 'A. Chinaev, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA, 2016.'
date_created: 2019-07-12T05:27:18Z
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/2016/ChHa16.pdf
oa: '1'
publication: INTERSPEECH 2016, San Francisco, USA
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHa16_Poster.pdf
status: public
title: A Priori SNR Estimation Using a Generalized Decision Directed Approach
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11743'
abstract:
- lang: eng
text: This contribution introduces a novel causal a priori signal-to-noise ratio
(SNR) estimator for single-channel speech enhancement. To exploit the advantages
of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM)
domain is introduced where an a priori SNR estimation is carried out. In this
domain, the NAOM coefficients of noise and clean speech signals are modeled by
a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the
parameters of the noise model are calculated from the noise power spectral density
estimates, the speechWMM parameters are estimated from the noisy signal by applying
a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate
of the a priori SNR is developed. The experiments in different noisy environments
show the superiority of the proposed estimator compared to the well-known decision-directed
approach in terms of estimation error, estimator variance and speech quality of
the enhanced signals when used for speech enhancement.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Heitkaemper J, Haeb-Umbach R. A Priori SNR Estimation Using Weibull
Mixture Model. In: 12. ITG Fachtagung Sprachkommunikation (ITG 2016). ;
2016.'
apa: Chinaev, A., Heitkaemper, J., & Haeb-Umbach, R. (2016). A Priori SNR Estimation
Using Weibull Mixture Model. In 12. ITG Fachtagung Sprachkommunikation (ITG
2016).
bibtex: '@inproceedings{Chinaev_Heitkaemper_Haeb-Umbach_2016, title={A Priori SNR
Estimation Using Weibull Mixture Model}, booktitle={12. ITG Fachtagung Sprachkommunikation
(ITG 2016)}, author={Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold},
year={2016} }'
chicago: Chinaev, Aleksej, Jens Heitkaemper, and Reinhold Haeb-Umbach. “A Priori
SNR Estimation Using Weibull Mixture Model.” In 12. ITG Fachtagung Sprachkommunikation
(ITG 2016), 2016.
ieee: A. Chinaev, J. Heitkaemper, and R. Haeb-Umbach, “A Priori SNR Estimation Using
Weibull Mixture Model,” in 12. ITG Fachtagung Sprachkommunikation (ITG 2016),
2016.
mla: Chinaev, Aleksej, et al. “A Priori SNR Estimation Using Weibull Mixture Model.”
12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.
short: 'A. Chinaev, J. Heitkaemper, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation
(ITG 2016), 2016.'
date_created: 2019-07-12T05:27:24Z
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/2016/ChHeiHa16.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16_Presentation.pdf
status: public
title: A Priori SNR Estimation Using Weibull Mixture Model
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11744'
abstract:
- lang: eng
text: A noise power spectral density (PSD) estimation is an indispensable component
of speech spectral enhancement systems. In this paper we present a noise PSD tracking
algorithm, which employs a noise presence probability estimate delivered by a
deep neural network (DNN). The algorithm provides a causal noise PSD estimate
and can thus be used in speech enhancement systems for communication purposes.
An extensive performance comparison has been carried out with ten causal state-of-the-art
noise tracking algorithms taken from the literature and categorized acc. to applied
techniques. The experiments showed that the proposed DNN-based noise PSD tracker
outperforms all competing methods with respect to all tested performance measures,
which include the noise tracking performance and the performance of a speech enhancement
system employing the noise tracking component.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- 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: 'Chinaev A, Heymann J, Drude L, Haeb-Umbach R. Noise-Presence-Probability-Based
Noise PSD Estimation by Using DNNs. In: 12. ITG Fachtagung Sprachkommunikation
(ITG 2016). ; 2016.'
apa: Chinaev, A., Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Noise-Presence-Probability-Based
Noise PSD Estimation by Using DNNs. In 12. ITG Fachtagung Sprachkommunikation
(ITG 2016).
bibtex: '@inproceedings{Chinaev_Heymann_Drude_Haeb-Umbach_2016, title={Noise-Presence-Probability-Based
Noise PSD Estimation by Using DNNs}, booktitle={12. ITG Fachtagung Sprachkommunikation
(ITG 2016)}, author={Chinaev, Aleksej and Heymann, Jahn and Drude, Lukas and Haeb-Umbach,
Reinhold}, year={2016} }'
chicago: Chinaev, Aleksej, Jahn Heymann, Lukas Drude, and Reinhold Haeb-Umbach.
“Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs.” In 12.
ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.
ieee: A. Chinaev, J. Heymann, L. Drude, and R. Haeb-Umbach, “Noise-Presence-Probability-Based
Noise PSD Estimation by Using DNNs,” in 12. ITG Fachtagung Sprachkommunikation
(ITG 2016), 2016.
mla: Chinaev, Aleksej, et al. “Noise-Presence-Probability-Based Noise PSD Estimation
by Using DNNs.” 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.
short: 'A. Chinaev, J. Heymann, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung
Sprachkommunikation (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:25Z
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/2016/ChHeyDrHa16.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeyDrHa16_Presentation.pdf
status: public
title: Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11751'
author:
- 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: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Boeddeker C, Haeb-Umbach R. Blind Speech Separation based on Complex
Spherical k-Mode Clustering. In: Proc. IEEE Intl. Conf. on Acoustics, Speech
and Signal Processing (ICASSP). ; 2016.'
apa: Drude, L., Boeddeker, C., & Haeb-Umbach, R. (2016). Blind Speech Separation
based on Complex Spherical k-Mode Clustering. In Proc. IEEE Intl. Conf. on
Acoustics, Speech and Signal Processing (ICASSP).
bibtex: '@inproceedings{Drude_Boeddeker_Haeb-Umbach_2016, title={Blind Speech Separation
based on Complex Spherical k-Mode Clustering}, booktitle={Proc. IEEE Intl. Conf.
on Acoustics, Speech and Signal Processing (ICASSP)}, author={Drude, Lukas and
Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2016} }'
chicago: Drude, Lukas, Christoph Boeddeker, and Reinhold Haeb-Umbach. “Blind Speech
Separation Based on Complex Spherical K-Mode Clustering.” In Proc. IEEE Intl.
Conf. on Acoustics, Speech and Signal Processing (ICASSP), 2016.
ieee: L. Drude, C. Boeddeker, and R. Haeb-Umbach, “Blind Speech Separation based
on Complex Spherical k-Mode Clustering,” in Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2016.
mla: Drude, Lukas, et al. “Blind Speech Separation Based on Complex Spherical K-Mode
Clustering.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP), 2016.
short: 'L. Drude, C. Boeddeker, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2016.'
date_created: 2019-07-12T05:27:33Z
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/2016/icassp_2016_drude_paper.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/2016/icassp_2016_drude_slides.pdf
status: public
title: Blind Speech Separation based on Complex Spherical k-Mode Clustering
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11756'
abstract:
- lang: eng
text: Although complex-valued neural networks (CVNNs) â?? networks which can operate
with complex arithmetic â?? have been around for a while, they have not been given
reconsideration since the breakthrough of deep network architectures. This paper
presents a critical assessment whether the novel tool set of deep neural networks
(DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs making
inroads in speech enhancement tasks, the use of complex-valued input data, specifically
the short-time Fourier transform coefficients, is an obvious consideration. In
particular when it comes to performing tasks that heavily rely on phase information,
such as acoustic beamforming, complex-valued algorithms are omnipresent. In this
contribution we recapitulate backpropagation in CVNNs, develop complex-valued
network elements, such as the split-rectified non-linearity, and compare real-
and complex-valued networks on a beamforming task. We find that CVNNs hardly provide
a performance gain and conclude that the effort of developing the complex-valued
counterparts of the building blocks of modern deep or recurrent neural networks
can hardly be justified.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Bhiksha
full_name: Raj, Bhiksha
last_name: Raj
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Raj B, Haeb-Umbach R. On the appropriateness of complex-valued neural
networks for speech enhancement. In: INTERSPEECH 2016, San Francisco, USA.
; 2016.'
apa: Drude, L., Raj, B., & Haeb-Umbach, R. (2016). On the appropriateness of
complex-valued neural networks for speech enhancement. In INTERSPEECH 2016,
San Francisco, USA.
bibtex: '@inproceedings{Drude_Raj_Haeb-Umbach_2016, title={On the appropriateness
of complex-valued neural networks for speech enhancement}, booktitle={INTERSPEECH
2016, San Francisco, USA}, author={Drude, Lukas and Raj, Bhiksha and Haeb-Umbach,
Reinhold}, year={2016} }'
chicago: Drude, Lukas, Bhiksha Raj, and Reinhold Haeb-Umbach. “On the Appropriateness
of Complex-Valued Neural Networks for Speech Enhancement.” In INTERSPEECH 2016,
San Francisco, USA, 2016.
ieee: L. Drude, B. Raj, and R. Haeb-Umbach, “On the appropriateness of complex-valued
neural networks for speech enhancement,” in INTERSPEECH 2016, San Francisco,
USA, 2016.
mla: Drude, Lukas, et al. “On the Appropriateness of Complex-Valued Neural Networks
for Speech Enhancement.” INTERSPEECH 2016, San Francisco, USA, 2016.
short: 'L. Drude, B. Raj, R. Haeb-Umbach, in: INTERSPEECH 2016, San Francisco, USA,
2016.'
date_created: 2019-07-12T05:27:39Z
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/2016/interspeech_2016_drude_paper.pdf
oa: '1'
publication: INTERSPEECH 2016, San Francisco, USA
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/interspeech_2016_drude_slides.pdf
status: public
title: On the appropriateness of complex-valued neural networks for speech enhancement
type: conference
user_id: '44006'
year: '2016'
...