---
_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: '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: '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: '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: '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: '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: '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: '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'
...
---
_id: '11771'
abstract:
- lang: eng
text: This paper is concerned with speech presence probability estimation employing
an explicit model of the temporal and spectral correlations of speech. An undirected
graphical model is introduced, based on a Factor Graph formulation. It is shown
that this undirected model cures some of the theoretical issues of an earlier
directed graphical model. Furthermore, we formulate a message passing inference
scheme based on an approximate graph factorization, identify this inference scheme
as a particular message passing schedule based on the turbo principle and suggest
further alternative schedules. The experiments show an improved performance over
speech presence probability estimation based on an IID assumption, and a slightly
better performance of the turbo schedule over the alternatives.
author:
- first_name: Thomas
full_name: Glarner, Thomas
id: '14169'
last_name: Glarner
- first_name: Mohammad
full_name: Mahdi Momenzadeh, Mohammad
last_name: Mahdi Momenzadeh
- 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: 'Glarner T, Mahdi Momenzadeh M, Drude L, Haeb-Umbach R. Factor Graph Decoding
for Speech Presence Probability Estimation. In: 12. ITG Fachtagung Sprachkommunikation
(ITG 2016). ; 2016.'
apa: Glarner, T., Mahdi Momenzadeh, M., Drude, L., & Haeb-Umbach, R. (2016).
Factor Graph Decoding for Speech Presence Probability Estimation. In 12. ITG
Fachtagung Sprachkommunikation (ITG 2016).
bibtex: '@inproceedings{Glarner_Mahdi Momenzadeh_Drude_Haeb-Umbach_2016, title={Factor
Graph Decoding for Speech Presence Probability Estimation}, booktitle={12. ITG
Fachtagung Sprachkommunikation (ITG 2016)}, author={Glarner, Thomas and Mahdi
Momenzadeh, Mohammad and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016}
}'
chicago: Glarner, Thomas, Mohammad Mahdi Momenzadeh, Lukas Drude, and Reinhold Haeb-Umbach.
“Factor Graph Decoding for Speech Presence Probability Estimation.” In 12.
ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.
ieee: T. Glarner, M. Mahdi Momenzadeh, L. Drude, and R. Haeb-Umbach, “Factor Graph
Decoding for Speech Presence Probability Estimation,” in 12. ITG Fachtagung
Sprachkommunikation (ITG 2016), 2016.
mla: Glarner, Thomas, et al. “Factor Graph Decoding for Speech Presence Probability
Estimation.” 12. ITG Fachtagung Sprachkommunikation (ITG 2016), 2016.
short: 'T. Glarner, M. Mahdi Momenzadeh, L. Drude, R. Haeb-Umbach, in: 12. ITG Fachtagung
Sprachkommunikation (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:56Z
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/itgspeech2016_08_Glarner.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
link:
- description: Slides
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/itgspeech2016_08_Glarner_slides.pdf
status: public
title: Factor Graph Decoding for Speech Presence Probability Estimation
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11812'
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. Neural Network Based Spectral Mask Estimation
for Acoustic Beamforming. In: Proc. IEEE Intl. Conf. on Acoustics, Speech and
Signal Processing (ICASSP). ; 2016.'
apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Neural Network Based
Spectral Mask Estimation for Acoustic Beamforming. In Proc. IEEE Intl. Conf.
on Acoustics, Speech and Signal Processing (ICASSP).
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Neural Network Based
Spectral Mask Estimation for Acoustic Beamforming}, booktitle={Proc. IEEE Intl.
Conf. on Acoustics, Speech and Signal Processing (ICASSP)}, author={Heymann, Jahn
and Drude, Lukas and Haeb-Umbach, Reinhold}, year={2016} }'
chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Neural Network Based
Spectral Mask Estimation for Acoustic Beamforming.” In Proc. IEEE Intl. Conf.
on Acoustics, Speech and Signal Processing (ICASSP), 2016.
ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Neural Network Based Spectral Mask
Estimation for Acoustic Beamforming,” in Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2016.
mla: Heymann, Jahn, et al. “Neural Network Based Spectral Mask Estimation for Acoustic
Beamforming.” Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing
(ICASSP), 2016.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Proc. IEEE Intl. Conf. on Acoustics,
Speech and Signal Processing (ICASSP), 2016.'
date_created: 2019-07-12T05:28:44Z
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/2016/icassp_2016_heymann_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_heymann_slides.pdf
status: public
title: Neural Network Based Spectral Mask Estimation for Acoustic Beamforming
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11834'
abstract:
- lang: eng
text: We present a system for the 4th CHiME challenge which significantly increases
the performance for all three tracks with respect to the provided baseline system.
The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural
network to estimate signal statistics. These then steer a Generalized Eigenvalue
beamformer. The back-end consists of a 22 layer deep Wide Residual Network and
two extra BLSTM layers. Working on a whole utterance instead of frames allows
us to refine Batch-Normalization. We also train our own BLSTM-based language model.
Adding a discriminative speaker adaptation leads to further gains. The final system
achieves a word error rate on the six channel real test data of 3.48%. For the
two channel track we achieve 5.96% and for the one channel track 9.34%. This is
the best reported performance on the challenge achieved by a single system, i.e.,
a configuration, which does not combine multiple systems. At the same time, our
system is independent of the microphone configuration. We can thus use the same
components for all three tracks.
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. Wide Residual BLSTM Network with Discriminative
Speaker Adaptation for Robust Speech Recognition. In: Computer Speech and Language.
; 2016.'
apa: Heymann, J., Drude, L., & Haeb-Umbach, R. (2016). Wide Residual BLSTM Network
with Discriminative Speaker Adaptation for Robust Speech Recognition. In Computer
Speech and Language.
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_2016, title={Wide Residual BLSTM
Network with Discriminative Speaker Adaptation for Robust Speech Recognition},
booktitle={Computer Speech and Language}, author={Heymann, Jahn and Drude, Lukas
and Haeb-Umbach, Reinhold}, year={2016} }'
chicago: Heymann, Jahn, Lukas Drude, and Reinhold Haeb-Umbach. “Wide Residual BLSTM
Network with Discriminative Speaker Adaptation for Robust Speech Recognition.”
In Computer Speech and Language, 2016.
ieee: J. Heymann, L. Drude, and R. Haeb-Umbach, “Wide Residual BLSTM Network with
Discriminative Speaker Adaptation for Robust Speech Recognition,” in Computer
Speech and Language, 2016.
mla: Heymann, Jahn, et al. “Wide Residual BLSTM Network with Discriminative Speaker
Adaptation for Robust Speech Recognition.” Computer Speech and Language,
2016.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, in: Computer Speech and Language,
2016.'
date_created: 2019-07-12T05:29:09Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_paper.pdf
oa: '1'
publication: Computer Speech and Language
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_upbonly_poster.pdf
status: public
title: Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust
Speech Recognition
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11908'
abstract:
- lang: eng
text: 'This paper describes automatic speech recognition (ASR) systems developed
jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME
Challenge. In the 2ch and 6ch tracks the final system output is obtained by a
Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM)
is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM)
units. The systems differ by front ends and training sets used for the acoustic
training. The model for the 1ch track is trained without any preprocessing. For
each front end we trained and evaluated individual acoustic models. We compare
the ASR performance of different beamforming approaches: a conventional superdirective
beamformer [1] and an MVDR beamformer as in [2], where the steering vector is
estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized
Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s
open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices
with a Long Short-Term Memory (LSTM) based language model. The overall best results
are obtained by a system combination that includes the lattices from the system
of UPB?s submission [8]. Our final submission scored second in each of the three
tracks of the 4th CHiME Challenge.'
author:
- first_name: Tobias
full_name: Menne, Tobias
last_name: Menne
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Anastasios
full_name: Alexandridis, Anastasios
last_name: Alexandridis
- first_name: Kazuki
full_name: Irie, Kazuki
last_name: Irie
- first_name: Albert
full_name: Zeyer, Albert
last_name: Zeyer
- first_name: Markus
full_name: Kitza, Markus
last_name: Kitza
- first_name: Pavel
full_name: Golik, Pavel
last_name: Golik
- first_name: Ilia
full_name: Kulikov, Ilia
last_name: Kulikov
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Ralf
full_name: Schlüter, Ralf
last_name: Schlüter
- first_name: Hermann
full_name: Ney, Hermann
last_name: Ney
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Athanasios
full_name: Mouchtaris, Athanasios
last_name: Mouchtaris
citation:
ama: 'Menne T, Heymann J, Alexandridis A, et al. The RWTH/UPB/FORTH System Combination
for the 4th CHiME Challenge Evaluation. In: Computer Speech and Language.
; 2016.'
apa: Menne, T., Heymann, J., Alexandridis, A., Irie, K., Zeyer, A., Kitza, M., …
Mouchtaris, A. (2016). The RWTH/UPB/FORTH System Combination for the 4th CHiME
Challenge Evaluation. In Computer Speech and Language.
bibtex: '@inproceedings{Menne_Heymann_Alexandridis_Irie_Zeyer_Kitza_Golik_Kulikov_Drude_Schlüter_et
al._2016, title={The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge
Evaluation}, booktitle={Computer Speech and Language}, author={Menne, Tobias and
Heymann, Jahn and Alexandridis, Anastasios and Irie, Kazuki and Zeyer, Albert
and Kitza, Markus and Golik, Pavel and Kulikov, Ilia and Drude, Lukas and Schlüter,
Ralf and et al.}, year={2016} }'
chicago: Menne, Tobias, Jahn Heymann, Anastasios Alexandridis, Kazuki Irie, Albert
Zeyer, Markus Kitza, Pavel Golik, et al. “The RWTH/UPB/FORTH System Combination
for the 4th CHiME Challenge Evaluation.” In Computer Speech and Language,
2016.
ieee: T. Menne et al., “The RWTH/UPB/FORTH System Combination for the 4th
CHiME Challenge Evaluation,” in Computer Speech and Language, 2016.
mla: Menne, Tobias, et al. “The RWTH/UPB/FORTH System Combination for the 4th CHiME
Challenge Evaluation.” Computer Speech and Language, 2016.
short: 'T. Menne, J. Heymann, A. Alexandridis, K. Irie, A. Zeyer, M. Kitza, P. Golik,
I. Kulikov, L. Drude, R. Schlüter, H. Ney, R. Haeb-Umbach, A. Mouchtaris, in:
Computer Speech and Language, 2016.'
date_created: 2019-07-12T05:30:35Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2016/chime4_rwthupbforth_paper.pdf
oa: '1'
publication: Computer Speech and Language
status: public
title: The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation
type: conference
user_id: '44006'
year: '2016'
...
---
_id: '11755'
abstract:
- lang: eng
text: This contribution presents a Direction of Arrival (DoA) estimation algorithm
based on the complex Watson distribution to incorporate both phase and level differences
of captured micro- phone array signals. The derived algorithm is reviewed in the
context of the Generalized State Coherence Transform (GSCT) on the one hand and
a kernel density estimation method on the other hand. A thorough simulative evaluation
yields insight into parameter selection and provides details on the performance
for both directional and omni-directional microphones. A comparison to the well
known Steered Response Power with Phase Transform (SRP-PHAT) algorithm and a state
of the art DoA estimator which explicitly accounts for aliasing, shows in particular
the advantages of presented algorithm if inter-sensor level differences are indicative
of the DoA, as with directional microphones.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Florian
full_name: Jacob, Florian
last_name: Jacob
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Jacob F, Haeb-Umbach R. DOA-Estimation based on a Complex Watson
Kernel Method. In: 23th European Signal Processing Conference (EUSIPCO 2015).
; 2015.'
apa: Drude, L., Jacob, F., & Haeb-Umbach, R. (2015). DOA-Estimation based on
a Complex Watson Kernel Method. In 23th European Signal Processing Conference
(EUSIPCO 2015).
bibtex: '@inproceedings{Drude_Jacob_Haeb-Umbach_2015, title={DOA-Estimation based
on a Complex Watson Kernel Method}, booktitle={23th European Signal Processing
Conference (EUSIPCO 2015)}, author={Drude, Lukas and Jacob, Florian and Haeb-Umbach,
Reinhold}, year={2015} }'
chicago: Drude, Lukas, Florian Jacob, and Reinhold Haeb-Umbach. “DOA-Estimation
Based on a Complex Watson Kernel Method.” In 23th European Signal Processing
Conference (EUSIPCO 2015), 2015.
ieee: L. Drude, F. Jacob, and R. Haeb-Umbach, “DOA-Estimation based on a Complex
Watson Kernel Method,” in 23th European Signal Processing Conference (EUSIPCO
2015), 2015.
mla: Drude, Lukas, et al. “DOA-Estimation Based on a Complex Watson Kernel Method.”
23th European Signal Processing Conference (EUSIPCO 2015), 2015.
short: 'L. Drude, F. Jacob, R. Haeb-Umbach, in: 23th European Signal Processing
Conference (EUSIPCO 2015), 2015.'
date_created: 2019-07-12T05:27:38Z
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/2015/DrJaHa15.pdf
oa: '1'
publication: 23th European Signal Processing Conference (EUSIPCO 2015)
related_material:
link:
- description: Presentation
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2015/DrJaHa15_Presentation.pdf
status: public
title: DOA-Estimation based on a Complex Watson Kernel Method
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11810'
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: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heymann J, Drude L, Chinaev A, Haeb-Umbach R. BLSTM supported GEV Beamformer
Front-End for the 3RD CHiME Challenge. In: Automatic Speech Recognition and
Understanding Workshop (ASRU 2015). ; 2015.'
apa: Heymann, J., Drude, L., Chinaev, A., & Haeb-Umbach, R. (2015). BLSTM supported
GEV Beamformer Front-End for the 3RD CHiME Challenge. In Automatic Speech Recognition
and Understanding Workshop (ASRU 2015).
bibtex: '@inproceedings{Heymann_Drude_Chinaev_Haeb-Umbach_2015, title={BLSTM supported
GEV Beamformer Front-End for the 3RD CHiME Challenge}, booktitle={Automatic Speech
Recognition and Understanding Workshop (ASRU 2015)}, author={Heymann, Jahn and
Drude, Lukas and Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2015} }'
chicago: Heymann, Jahn, Lukas Drude, Aleksej Chinaev, and Reinhold Haeb-Umbach.
“BLSTM Supported GEV Beamformer Front-End for the 3RD CHiME Challenge.” In Automatic
Speech Recognition and Understanding Workshop (ASRU 2015), 2015.
ieee: J. Heymann, L. Drude, A. Chinaev, and R. Haeb-Umbach, “BLSTM supported GEV
Beamformer Front-End for the 3RD CHiME Challenge,” in Automatic Speech Recognition
and Understanding Workshop (ASRU 2015), 2015.
mla: Heymann, Jahn, et al. “BLSTM Supported GEV Beamformer Front-End for the 3RD
CHiME Challenge.” Automatic Speech Recognition and Understanding Workshop (ASRU
2015), 2015.
short: 'J. Heymann, L. Drude, A. Chinaev, R. Haeb-Umbach, in: Automatic Speech Recognition
and Understanding Workshop (ASRU 2015), 2015.'
date_created: 2019-07-12T05:28:41Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
language:
- iso: eng
publication: Automatic Speech Recognition and Understanding Workshop (ASRU 2015)
status: public
title: BLSTM supported GEV Beamformer Front-End for the 3RD CHiME Challenge
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11919'
abstract:
- lang: eng
text: In this paper we present a source counting algorithm to determine the number
of speakers in a speech mixture. In our proposed method, we model the histogram
of estimated directions of arrival with a nonparametric Bayesian infinite Gaussian
mixture model. As an alternative to classical model selection criteria and to
avoid specifying the maximum number of mixture components in advance, a Dirichlet
process prior is employed over the mixture components. This allows to automatically
determine the optimal number of mixture components that most probably model the
observations. We demonstrate by experiments that this model outperforms a parametric
approach using a finite Gaussian mixture model with a Dirichlet distribution prior
over the mixture weights.
author:
- first_name: Oliver
full_name: Walter, Oliver
last_name: Walter
- 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: 'Walter O, Drude L, Haeb-Umbach R. Source Counting in Speech Mixtures by Nonparametric
Bayesian Estimation of an infinite Gaussian Mixture Model. In: 40th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2015). ; 2015.'
apa: Walter, O., Drude, L., & Haeb-Umbach, R. (2015). Source Counting in Speech
Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture
Model. In 40th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2015).
bibtex: '@inproceedings{Walter_Drude_Haeb-Umbach_2015, title={Source Counting in
Speech Mixtures by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture
Model}, booktitle={40th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2015)}, author={Walter, Oliver and Drude, Lukas and Haeb-Umbach,
Reinhold}, year={2015} }'
chicago: Walter, Oliver, Lukas Drude, and Reinhold Haeb-Umbach. “Source Counting
in Speech Mixtures by Nonparametric Bayesian Estimation of an Infinite Gaussian
Mixture Model.” In 40th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2015), 2015.
ieee: O. Walter, L. Drude, and R. Haeb-Umbach, “Source Counting in Speech Mixtures
by Nonparametric Bayesian Estimation of an infinite Gaussian Mixture Model,” in
40th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2015), 2015.
mla: Walter, Oliver, et al. “Source Counting in Speech Mixtures by Nonparametric
Bayesian Estimation of an Infinite Gaussian Mixture Model.” 40th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.
short: 'O. Walter, L. Drude, R. Haeb-Umbach, in: 40th International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2015), 2015.'
date_created: 2019-07-12T05:30:47Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/WaDrHa15.pdf
oa: '1'
publication: 40th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2015)
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2015/WaDrHa15_Poster.pdf
status: public
title: Source Counting in Speech Mixtures by Nonparametric Bayesian Estimation of
an infinite Gaussian Mixture Model
type: conference
user_id: '44006'
year: '2015'
...