---
_id: '22528'
abstract:
- lang: eng
text: Due to the ad hoc nature of wireless acoustic sensor networks, the position
of the sensor nodes is typically unknown. This contribution proposes a technique
to estimate the position and orientation of the sensor nodes from the recorded
speech signals. The method assumes that a node comprises a microphone array with
synchronously sampled microphones rather than a single microphone, but does not
require the sampling clocks of the nodes to be synchronized. From the observed
audio signals, the distances between the acoustic sources and arrays, as well
as the directions of arrival, are estimated. They serve as input to a non-linear
least squares problem, from which both the sensor nodes’ positions and orientations,
as well as the source positions, are alternatingly estimated in an iterative process.
Given one set of unknowns, i.e., either the source positions or the sensor nodes’
geometry, the other set of unknowns can be computed in closed-form. The proposed
approach is computationally efficient and the first one, which employs both distance
and directional information for geometry calibration in a common cost function.
Since both distance and direction of arrival measurements suffer from outliers,
e.g., caused by strong reflections of the sound waves on the surfaces of the room,
we introduce measures to deemphasize or remove unreliable measurements. Additionally,
we discuss modifications of our previously proposed deep neural network-based
acoustic distance estimator, to account not only for omnidirectional sources but
also for directional sources. Simulation results show good positioning accuracy
and compare very favorably with alternative approaches from the literature.
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Geometry calibration in wireless
acoustic sensor networks utilizing DoA and distance information. EURASIP Journal
on Audio, Speech, and Music Processing. Published online 2021. doi:10.1186/s13636-021-00210-x
apa: Gburrek, T., Schmalenstroeer, J., & Haeb-Umbach, R. (2021). Geometry calibration
in wireless acoustic sensor networks utilizing DoA and distance information. EURASIP
Journal on Audio, Speech, and Music Processing. https://doi.org/10.1186/s13636-021-00210-x
bibtex: '@article{Gburrek_Schmalenstroeer_Haeb-Umbach_2021, title={Geometry calibration
in wireless acoustic sensor networks utilizing DoA and distance information},
DOI={10.1186/s13636-021-00210-x},
journal={EURASIP Journal on Audio, Speech, and Music Processing}, author={Gburrek,
Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2021} }'
chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Geometry
Calibration in Wireless Acoustic Sensor Networks Utilizing DoA and Distance Information.”
EURASIP Journal on Audio, Speech, and Music Processing, 2021. https://doi.org/10.1186/s13636-021-00210-x.
ieee: 'T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Geometry calibration
in wireless acoustic sensor networks utilizing DoA and distance information,”
EURASIP Journal on Audio, Speech, and Music Processing, 2021, doi: 10.1186/s13636-021-00210-x.'
mla: Gburrek, Tobias, et al. “Geometry Calibration in Wireless Acoustic Sensor Networks
Utilizing DoA and Distance Information.” EURASIP Journal on Audio, Speech,
and Music Processing, 2021, doi:10.1186/s13636-021-00210-x.
short: T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, EURASIP Journal on Audio,
Speech, and Music Processing (2021).
date_created: 2021-07-05T05:30:15Z
date_updated: 2023-11-17T06:36:17Z
department:
- _id: '54'
doi: 10.1186/s13636-021-00210-x
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-021-00210-x
oa: '1'
publication: EURASIP Journal on Audio, Speech, and Music Processing
publication_identifier:
issn:
- 1687-4722
publication_status: published
quality_controlled: '1'
status: public
title: Geometry calibration in wireless acoustic sensor networks utilizing DoA and
distance information
type: journal_article
user_id: '44006'
year: '2021'
...
---
_id: '23994'
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Iterative Geometry Calibration
from Distance Estimates for Wireless Acoustic Sensor Networks. In: ICASSP 2021
- 2021 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP). ; 2021. doi:10.1109/icassp39728.2021.9413831'
apa: Gburrek, T., Schmalenstroeer, J., & Haeb-Umbach, R. (2021). Iterative Geometry
Calibration from Distance Estimates for Wireless Acoustic Sensor Networks. ICASSP
2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP). https://doi.org/10.1109/icassp39728.2021.9413831
bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2021, title={Iterative
Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks},
DOI={10.1109/icassp39728.2021.9413831},
booktitle={ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)}, author={Gburrek, Tobias and Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2021} }'
chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Iterative
Geometry Calibration from Distance Estimates for Wireless Acoustic Sensor Networks.”
In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP), 2021. https://doi.org/10.1109/icassp39728.2021.9413831.
ieee: 'T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Iterative Geometry Calibration
from Distance Estimates for Wireless Acoustic Sensor Networks,” 2021, doi: 10.1109/icassp39728.2021.9413831.'
mla: Gburrek, Tobias, et al. “Iterative Geometry Calibration from Distance Estimates
for Wireless Acoustic Sensor Networks.” ICASSP 2021 - 2021 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021, doi:10.1109/icassp39728.2021.9413831.
short: 'T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: ICASSP 2021 - 2021 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2021.'
date_created: 2021-09-09T08:30:16Z
date_updated: 2023-11-17T06:30:12Z
ddc:
- '004'
department:
- _id: '54'
doi: 10.1109/icassp39728.2021.9413831
file:
- access_level: open_access
content_type: application/pdf
creator: tgburrek
date_created: 2023-11-17T06:29:40Z
date_updated: 2023-11-17T06:30:11Z
file_id: '48988'
file_name: icassp21.pdf
file_size: 312400
relation: main_file
file_date_updated: 2023-11-17T06:30:11Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
publication_status: published
quality_controlled: '1'
status: public
title: Iterative Geometry Calibration from Distance Estimates for Wireless Acoustic
Sensor Networks
type: conference
user_id: '44006'
year: '2021'
...
---
_id: '23999'
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Gburrek T, Schmalenstroeer J, Haeb-Umbach R. On Source-Microphone Distance
Estimation Using Convolutional Recurrent Neural Networks. In: Speech Communication;
14th ITG-Symposium. ; 2021:1-5.'
apa: Gburrek, T., Schmalenstroeer, J., & Haeb-Umbach, R. (2021). On Source-Microphone
Distance Estimation Using Convolutional Recurrent Neural Networks. Speech Communication;
14th ITG-Symposium, 1–5.
bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2021, title={On Source-Microphone
Distance Estimation Using Convolutional Recurrent Neural Networks}, booktitle={Speech
Communication; 14th ITG-Symposium}, author={Gburrek, Tobias and Schmalenstroeer,
Joerg and Haeb-Umbach, Reinhold}, year={2021}, pages={1–5} }'
chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “On Source-Microphone
Distance Estimation Using Convolutional Recurrent Neural Networks.” In Speech
Communication; 14th ITG-Symposium, 1–5, 2021.
ieee: T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “On Source-Microphone
Distance Estimation Using Convolutional Recurrent Neural Networks,” in Speech
Communication; 14th ITG-Symposium, 2021, pp. 1–5.
mla: Gburrek, Tobias, et al. “On Source-Microphone Distance Estimation Using Convolutional
Recurrent Neural Networks.” Speech Communication; 14th ITG-Symposium, 2021,
pp. 1–5.
short: 'T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: Speech Communication;
14th ITG-Symposium, 2021, pp. 1–5.'
date_created: 2021-09-09T08:40:44Z
date_updated: 2023-11-17T06:32:20Z
ddc:
- '004'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: tgburrek
date_created: 2023-11-17T06:31:37Z
date_updated: 2023-11-17T06:31:37Z
file_id: '48989'
file_name: dist_est.pdf
file_size: 449694
relation: main_file
file_date_updated: 2023-11-17T06:31:37Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 1-5
publication: Speech Communication; 14th ITG-Symposium
quality_controlled: '1'
status: public
title: On Source-Microphone Distance Estimation Using Convolutional Recurrent Neural
Networks
type: conference
user_id: '44006'
year: '2021'
...
---
_id: '23997'
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Gerald
full_name: Enzner, Gerald
last_name: Enzner
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
citation:
ama: 'Chinaev A, Enzner G, Gburrek T, Schmalenstroeer J. Online Estimation of Sampling
Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss. In: 29th
European Signal Processing Conference (EUSIPCO). ; 2021:1-5.'
apa: Chinaev, A., Enzner, G., Gburrek, T., & Schmalenstroeer, J. (2021). Online
Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with
Packet Loss. 29th European Signal Processing Conference (EUSIPCO), 1–5.
bibtex: '@inproceedings{Chinaev_Enzner_Gburrek_Schmalenstroeer_2021, title={Online
Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with
Packet Loss}, booktitle={29th European Signal Processing Conference (EUSIPCO)},
author={Chinaev, Aleksej and Enzner, Gerald and Gburrek, Tobias and Schmalenstroeer,
Joerg}, year={2021}, pages={1–5} }'
chicago: Chinaev, Aleksej, Gerald Enzner, Tobias Gburrek, and Joerg Schmalenstroeer.
“Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks
with Packet Loss.” In 29th European Signal Processing Conference (EUSIPCO),
1–5, 2021.
ieee: A. Chinaev, G. Enzner, T. Gburrek, and J. Schmalenstroeer, “Online Estimation
of Sampling Rate Offsets in Wireless Acoustic Sensor Networks with Packet Loss,”
in 29th European Signal Processing Conference (EUSIPCO), 2021, pp. 1–5.
mla: Chinaev, Aleksej, et al. “Online Estimation of Sampling Rate Offsets in Wireless
Acoustic Sensor Networks with Packet Loss.” 29th European Signal Processing
Conference (EUSIPCO), 2021, pp. 1–5.
short: 'A. Chinaev, G. Enzner, T. Gburrek, J. Schmalenstroeer, in: 29th European
Signal Processing Conference (EUSIPCO), 2021, pp. 1–5.'
date_created: 2021-09-09T08:39:06Z
date_updated: 2023-11-17T06:37:10Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://eurasip.org/Proceedings/Eusipco/Eusipco2021/pdfs/0001110.pdf
oa: '1'
page: 1-5
publication: 29th European Signal Processing Conference (EUSIPCO)
quality_controlled: '1'
status: public
title: Online Estimation of Sampling Rate Offsets in Wireless Acoustic Sensor Networks
with Packet Loss
type: conference
user_id: '44006'
year: '2021'
...
---
_id: '29304'
abstract:
- lang: eng
text: 'In this work we address disentanglement of style and content in speech signals.
We propose a fully convolutional variational autoencoder employing two encoders:
a content encoder and a style encoder. To foster disentanglement, we propose adversarial
contrastive predictive coding. This new disentanglement method does neither need
parallel data nor any supervision. We show that the proposed technique is capable
of separating speaker and content traits into the two different representations
and show competitive speaker-content disentanglement performance compared to other
unsupervised approaches. We further demonstrate an increased robustness of the
content representation against a train-test mismatch compared to spectral features,
when used for phone recognition.'
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Michael
full_name: Kuhlmann, Michael
id: '49871'
last_name: Kuhlmann
- first_name: Tobias
full_name: Cord-Landwehr, Tobias
id: '44393'
last_name: Cord-Landwehr
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Kuhlmann M, Cord-Landwehr T, Haeb-Umbach R. Contrastive Predictive
Coding Supported Factorized Variational Autoencoder for Unsupervised Learning
of Disentangled Speech Representations. In: Proceedings of the IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). ; 2021:3860–3864.'
apa: Ebbers, J., Kuhlmann, M., Cord-Landwehr, T., & Haeb-Umbach, R. (2021).
Contrastive Predictive Coding Supported Factorized Variational Autoencoder for
Unsupervised Learning of Disentangled Speech Representations. Proceedings of
the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP),
3860–3864.
bibtex: '@inproceedings{Ebbers_Kuhlmann_Cord-Landwehr_Haeb-Umbach_2021, title={Contrastive
Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised
Learning of Disentangled Speech Representations}, booktitle={Proceedings of the
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
author={Ebbers, Janek and Kuhlmann, Michael and Cord-Landwehr, Tobias and Haeb-Umbach,
Reinhold}, year={2021}, pages={3860–3864} }'
chicago: Ebbers, Janek, Michael Kuhlmann, Tobias Cord-Landwehr, and Reinhold Haeb-Umbach.
“Contrastive Predictive Coding Supported Factorized Variational Autoencoder for
Unsupervised Learning of Disentangled Speech Representations.” In Proceedings
of the IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 3860–3864, 2021.
ieee: J. Ebbers, M. Kuhlmann, T. Cord-Landwehr, and R. Haeb-Umbach, “Contrastive
Predictive Coding Supported Factorized Variational Autoencoder for Unsupervised
Learning of Disentangled Speech Representations,” in Proceedings of the IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
2021, pp. 3860–3864.
mla: Ebbers, Janek, et al. “Contrastive Predictive Coding Supported Factorized Variational
Autoencoder for Unsupervised Learning of Disentangled Speech Representations.”
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2021, pp. 3860–3864.
short: 'J. Ebbers, M. Kuhlmann, T. Cord-Landwehr, R. Haeb-Umbach, in: Proceedings
of the IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2021, pp. 3860–3864.'
date_created: 2022-01-13T07:55:29Z
date_updated: 2023-11-22T08:29:42Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: ebbers
date_created: 2022-01-13T07:56:30Z
date_updated: 2022-01-13T08:19:19Z
file_id: '29305'
file_name: Template.pdf
file_size: 236628
relation: main_file
file_date_updated: 2022-01-13T08:19:19Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 3860–3864
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
quality_controlled: '1'
status: public
title: Contrastive Predictive Coding Supported Factorized Variational Autoencoder
for Unsupervised Learning of Disentangled Speech Representations
type: conference
user_id: '34851'
year: '2021'
...
---
_id: '26770'
abstract:
- lang: eng
text: "Automatic transcription of meetings requires handling of overlapped speech,
which calls for continuous speech separation (CSS) systems. The uPIT criterion
was proposed for utterance-level separation with neural networks and introduces
the constraint that the total number of speakers must not exceed the number of
output channels. When processing meeting-like data in a segment-wise manner, i.e.,
by separating overlapping segments independently and stitching adjacent segments
to continuous output streams, this constraint has to be fulfilled for any segment.
In this contribution, we show that this constraint can be significantly relaxed.
We propose a novel graph-based PIT criterion, which casts the assignment of utterances
to output channels in a graph coloring problem. It only requires that the number
of concurrently active speakers must not exceed the number of output channels.
As a consequence, the system can process an arbitrary number of speakers and arbitrarily
long segments and thus can handle more diverse scenarios.\r\nFurther, the stitching
algorithm for obtaining a consistent output order in neighboring segments is of
less importance and can even be eliminated completely, not the least reducing
the computational effort. Experiments on meeting-style WSJ data show improvements
in recognition performance over using the uPIT criterion. "
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Kinoshita K, Boeddeker C, Delcroix M, Haeb-Umbach R. Graph-PIT:
Generalized Permutation Invariant Training for Continuous Separation of Arbitrary
Numbers of Speakers. In: Interspeech 2021. ; 2021. doi:10.21437/interspeech.2021-1177'
apa: 'von Neumann, T., Kinoshita, K., Boeddeker, C., Delcroix, M., & Haeb-Umbach,
R. (2021). Graph-PIT: Generalized Permutation Invariant Training for Continuous
Separation of Arbitrary Numbers of Speakers. Interspeech 2021. Interspeech.
https://doi.org/10.21437/interspeech.2021-1177'
bibtex: '@inproceedings{von Neumann_Kinoshita_Boeddeker_Delcroix_Haeb-Umbach_2021,
title={Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers}, DOI={10.21437/interspeech.2021-1177},
booktitle={Interspeech 2021}, author={von Neumann, Thilo and Kinoshita, Keisuke
and Boeddeker, Christoph and Delcroix, Marc and Haeb-Umbach, Reinhold}, year={2021}
}'
chicago: 'Neumann, Thilo von, Keisuke Kinoshita, Christoph Boeddeker, Marc Delcroix,
and Reinhold Haeb-Umbach. “Graph-PIT: Generalized Permutation Invariant Training
for Continuous Separation of Arbitrary Numbers of Speakers.” In Interspeech
2021, 2021. https://doi.org/10.21437/interspeech.2021-1177.'
ieee: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, and R. Haeb-Umbach,
“Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers,” presented at the Interspeech, 2021, doi: 10.21437/interspeech.2021-1177.'
mla: 'von Neumann, Thilo, et al. “Graph-PIT: Generalized Permutation Invariant Training
for Continuous Separation of Arbitrary Numbers of Speakers.” Interspeech 2021,
2021, doi:10.21437/interspeech.2021-1177.'
short: 'T. von Neumann, K. Kinoshita, C. Boeddeker, M. Delcroix, R. Haeb-Umbach,
in: Interspeech 2021, 2021.'
conference:
name: Interspeech
date_created: 2021-10-25T08:50:01Z
date_updated: 2023-11-15T12:14:40Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.21437/interspeech.2021-1177
file:
- access_level: open_access
content_type: video/mp4
creator: tvn
date_created: 2021-12-06T10:39:13Z
date_updated: 2021-12-06T10:48:30Z
file_id: '28327'
file_name: Interspeech 2021 voiceover-002-compressed.mp4
file_size: 9550220
relation: supplementary_material
title: Video for INTERSPEECH 2021
- access_level: open_access
content_type: application/vnd.openxmlformats-officedocument.presentationml.presentation
creator: tvn
date_created: 2021-12-06T10:47:01Z
date_updated: 2021-12-06T10:47:01Z
file_id: '28328'
file_name: Graph-PIT-poster-presentation.pptx
file_size: 1337297
relation: slides
title: Slides from INTERSPEECH 2021
- access_level: open_access
content_type: application/pdf
creator: tvn
date_created: 2021-12-06T10:48:21Z
date_updated: 2021-12-06T10:48:21Z
file_id: '28329'
file_name: INTERSPEECH2021_Graph_PIT.pdf
file_size: 226589
relation: main_file
file_date_updated: 2021-12-06T10:48:30Z
has_accepted_license: '1'
keyword:
- Continuous speech separation
- automatic speech recognition
- overlapped speech
- permutation invariant training
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Interspeech 2021
publication_status: published
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/fgnt/graph_pit
status: public
title: 'Graph-PIT: Generalized Permutation Invariant Training for Continuous Separation
of Arbitrary Numbers of Speakers'
type: conference
user_id: '49870'
year: '2021'
...
---
_id: '29173'
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'von Neumann T, Boeddeker C, Kinoshita K, Delcroix M, Haeb-Umbach R. Speeding
Up Permutation Invariant Training for Source Separation. In: Speech Communication;
14th ITG Conference. ; 2021.'
apa: von Neumann, T., Boeddeker, C., Kinoshita, K., Delcroix, M., & Haeb-Umbach,
R. (2021). Speeding Up Permutation Invariant Training for Source Separation. Speech
Communication; 14th ITG Conference. Speech Communication; 14th ITG Conference,
Kiel.
bibtex: '@inproceedings{von Neumann_Boeddeker_Kinoshita_Delcroix_Haeb-Umbach_2021,
title={Speeding Up Permutation Invariant Training for Source Separation}, booktitle={Speech
Communication; 14th ITG Conference}, author={von Neumann, Thilo and Boeddeker,
Christoph and Kinoshita, Keisuke and Delcroix, Marc and Haeb-Umbach, Reinhold},
year={2021} }'
chicago: Neumann, Thilo von, Christoph Boeddeker, Keisuke Kinoshita, Marc Delcroix,
and Reinhold Haeb-Umbach. “Speeding Up Permutation Invariant Training for Source
Separation.” In Speech Communication; 14th ITG Conference, 2021.
ieee: T. von Neumann, C. Boeddeker, K. Kinoshita, M. Delcroix, and R. Haeb-Umbach,
“Speeding Up Permutation Invariant Training for Source Separation,” presented
at the Speech Communication; 14th ITG Conference, Kiel, 2021.
mla: von Neumann, Thilo, et al. “Speeding Up Permutation Invariant Training for
Source Separation.” Speech Communication; 14th ITG Conference, 2021.
short: 'T. von Neumann, C. Boeddeker, K. Kinoshita, M. Delcroix, R. Haeb-Umbach,
in: Speech Communication; 14th ITG Conference, 2021.'
conference:
end_date: 2021-10-01
location: Kiel
name: Speech Communication; 14th ITG Conference
start_date: 2021-09-29
date_created: 2022-01-07T10:40:56Z
date_updated: 2023-11-15T12:16:31Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: tvn
date_created: 2022-01-06T13:23:27Z
date_updated: 2022-01-06T13:23:27Z
file_id: '29180'
file_name: poster.pdf
file_size: 191938
relation: poster
- access_level: open_access
content_type: application/pdf
creator: tvn
date_created: 2022-01-07T10:42:54Z
date_updated: 2022-01-07T10:42:54Z
file_id: '29181'
file_name: ITG2021_Speeding_up_Permutation_Invariant_Training.pdf
file_size: 236670
relation: main_file
file_date_updated: 2022-01-07T10:42:54Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Speech Communication; 14th ITG Conference
quality_controlled: '1'
status: public
title: Speeding Up Permutation Invariant Training for Source Separation
type: conference
user_id: '49870'
year: '2021'
...
---
_id: '29308'
abstract:
- lang: eng
text: 'In this paper we present our system for the Detection and Classification
of Acoustic Scenes and Events (DCASE) 2021 Challenge Task 4: Sound Event Detection
and Separation in Domestic Environments, where it scored the fourth rank. Our
presented solution is an advancement of our system used in the previous edition
of the task.We use a forward-backward convolutional recurrent neural network (FBCRNN)
for tagging and pseudo labeling followed by tag-conditioned sound event detection
(SED) models which are trained using strong pseudo labels provided by the FBCRNN.
Our advancement over our earlier model is threefold. First, we introduce a strong
label loss in the objective of the FBCRNN to take advantage of the strongly labeled
synthetic data during training. Second, we perform multiple iterations of self-training
for both the FBCRNN and tag-conditioned SED models. Third, while we used only
tag-conditioned CNNs as our SED model in the previous edition we here explore
sophisticated tag-conditioned SED model architectures, namely, bidirectional CRNNs
and bidirectional convolutional transformer neural networks (CTNNs), and combine
them. With metric and class specific tuning of median filter lengths for post-processing,
our final SED model, consisting of 6 submodels (2 of each architecture), achieves
on the public evaluation set poly-phonic sound event detection scores (PSDS) of
0.455 for scenario 1 and 0.684 for scenario as well as a collar-based F1-score
of 0.596 outperforming the baselines and our model from the previous edition by
far. Source code is publicly available at https://github.com/fgnt/pb_sed.'
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Haeb-Umbach R. Self-Trained Audio Tagging and Sound Event Detection
in Domestic Environments. In: Proceedings of the 6th Detection and Classification
of Acoustic Scenes and Events 2021 Workshop (DCASE2021). ; 2021:226–230.'
apa: Ebbers, J., & Haeb-Umbach, R. (2021). Self-Trained Audio Tagging and Sound
Event Detection in Domestic Environments. Proceedings of the 6th Detection
and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021),
226–230.
bibtex: '@inproceedings{Ebbers_Haeb-Umbach_2021, place={Barcelona, Spain}, title={Self-Trained
Audio Tagging and Sound Event Detection in Domestic Environments}, booktitle={Proceedings
of the 6th Detection and Classification of Acoustic Scenes and Events 2021 Workshop
(DCASE2021)}, author={Ebbers, Janek and Haeb-Umbach, Reinhold}, year={2021}, pages={226–230}
}'
chicago: Ebbers, Janek, and Reinhold Haeb-Umbach. “Self-Trained Audio Tagging and
Sound Event Detection in Domestic Environments.” In Proceedings of the 6th
Detection and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021),
226–230. Barcelona, Spain, 2021.
ieee: J. Ebbers and R. Haeb-Umbach, “Self-Trained Audio Tagging and Sound Event
Detection in Domestic Environments,” in Proceedings of the 6th Detection and
Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021), 2021,
pp. 226–230.
mla: Ebbers, Janek, and Reinhold Haeb-Umbach. “Self-Trained Audio Tagging and Sound
Event Detection in Domestic Environments.” Proceedings of the 6th Detection
and Classification of Acoustic Scenes and Events 2021 Workshop (DCASE2021),
2021, pp. 226–230.
short: 'J. Ebbers, R. Haeb-Umbach, in: Proceedings of the 6th Detection and Classification
of Acoustic Scenes and Events 2021 Workshop (DCASE2021), Barcelona, Spain, 2021,
pp. 226–230.'
date_created: 2022-01-13T08:07:47Z
date_updated: 2023-11-22T08:28:32Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: ebbers
date_created: 2022-01-13T08:08:54Z
date_updated: 2022-01-13T08:19:50Z
file_id: '29309'
file_name: template.pdf
file_size: 239462
relation: main_file
file_date_updated: 2022-01-13T08:19:50Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 226–230
place: Barcelona, Spain
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the 6th Detection and Classification of Acoustic Scenes
and Events 2021 Workshop (DCASE2021)
publication_identifier:
isbn:
- 978-84-09-36072-7
quality_controlled: '1'
status: public
title: Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments
type: conference
user_id: '34851'
year: '2021'
...
---
_id: '29306'
abstract:
- lang: eng
text: Recently, there has been a rising interest in sound recognition via Acoustic
Sensor Networks to support applications such as ambient assisted living or environmental
habitat monitoring. With state-of-the-art sound recognition being dominated by
deep-learning-based approaches, there is a high demand for labeled training data.
Despite the availability of large-scale data sets such as Google's AudioSet,
acquiring training data matching a certain application environment is still often
a problem. In this paper we are concerned with human activity monitoring in a
domestic environment using an ASN consisting of multiple nodes each providing
multichannel signals. We propose a self-training based domain adaptation approach,
which only requires unlabeled data from the target environment. Here, a sound
recognition system trained on AudioSet, the teacher, generates pseudo labels for
data from the target environment on which a student network is trained. The student
can furthermore glean information about the spatial arrangement of sensors and
sound sources to further improve classification performance. It is shown that the
student significantly improves recognition performance over the pre-trained teacher
without relying on labeled data from the environment the system is deployed in.
author:
- first_name: Janek
full_name: Ebbers, Janek
id: '34851'
last_name: Ebbers
- first_name: Moritz Curt
full_name: Keyser, Moritz Curt
last_name: Keyser
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Ebbers J, Keyser MC, Haeb-Umbach R. Adapting Sound Recognition to A New Environment
Via Self-Training. In: Proceedings of the 29th European Signal Processing Conference
(EUSIPCO). ; 2021:1135–1139.'
apa: Ebbers, J., Keyser, M. C., & Haeb-Umbach, R. (2021). Adapting Sound Recognition
to A New Environment Via Self-Training. Proceedings of the 29th European Signal
Processing Conference (EUSIPCO), 1135–1139.
bibtex: '@inproceedings{Ebbers_Keyser_Haeb-Umbach_2021, title={Adapting Sound Recognition
to A New Environment Via Self-Training}, booktitle={Proceedings of the 29th European
Signal Processing Conference (EUSIPCO)}, author={Ebbers, Janek and Keyser, Moritz
Curt and Haeb-Umbach, Reinhold}, year={2021}, pages={1135–1139} }'
chicago: Ebbers, Janek, Moritz Curt Keyser, and Reinhold Haeb-Umbach. “Adapting
Sound Recognition to A New Environment Via Self-Training.” In Proceedings of
the 29th European Signal Processing Conference (EUSIPCO), 1135–1139, 2021.
ieee: J. Ebbers, M. C. Keyser, and R. Haeb-Umbach, “Adapting Sound Recognition to
A New Environment Via Self-Training,” in Proceedings of the 29th European Signal
Processing Conference (EUSIPCO), 2021, pp. 1135–1139.
mla: Ebbers, Janek, et al. “Adapting Sound Recognition to A New Environment Via
Self-Training.” Proceedings of the 29th European Signal Processing Conference
(EUSIPCO), 2021, pp. 1135–1139.
short: 'J. Ebbers, M.C. Keyser, R. Haeb-Umbach, in: Proceedings of the 29th European
Signal Processing Conference (EUSIPCO), 2021, pp. 1135–1139.'
date_created: 2022-01-13T08:01:21Z
date_updated: 2023-11-22T08:28:50Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: ebbers
date_created: 2022-01-13T08:03:26Z
date_updated: 2022-01-13T08:19:35Z
file_id: '29307'
file_name: conference_101719.pdf
file_size: 213938
relation: main_file
file_date_updated: 2022-01-13T08:19:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 1135–1139
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the 29th European Signal Processing Conference (EUSIPCO)
quality_controlled: '1'
status: public
title: Adapting Sound Recognition to A New Environment Via Self-Training
type: conference
user_id: '34851'
year: '2021'
...
---
_id: '24456'
abstract:
- lang: eng
text: One objective of current research in explainable intelligent systems is to
implement social aspects in order to increase the relevance of explanations. In
this paper, we argue that a novel conceptual framework is needed to overcome shortcomings
of existing AI systems with little attention to processes of interaction and learning.
Drawing from research in interaction and development, we first outline the novel
conceptual framework that pushes the design of AI systems toward true interactivity
with an emphasis on the role of the partner and social relevance. We propose that
AI systems will be able to provide a meaningful and relevant explanation only
if the process of explaining is extended to active contribution of both partners
that brings about dynamics that is modulated by different levels of analysis.
Accordingly, our conceptual framework comprises monitoring and scaffolding as
key concepts and claims that the process of explaining is not only modulated by
the interaction between explainee and explainer but is embedded into a larger
social context in which conventionalized and routinized behaviors are established.
We discuss our conceptual framework in relation to the established objectives
of transparency and autonomy that are raised for the design of explainable AI
systems currently.
article_type: original
author:
- first_name: Katharina J.
full_name: Rohlfing, Katharina J.
id: '50352'
last_name: Rohlfing
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Tobias
full_name: Matzner, Tobias
id: '65695'
last_name: Matzner
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
- first_name: Hendrik
full_name: Buschmeier, Hendrik
last_name: Buschmeier
- first_name: Elena
full_name: Esposito, Elena
last_name: Esposito
- first_name: Angela
full_name: Grimminger, Angela
id: '57578'
last_name: Grimminger
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Ilona
full_name: Horwath, Ilona
id: '68836'
last_name: Horwath
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Friederike
full_name: Kern, Friederike
last_name: Kern
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: 'Rohlfing KJ, Cimiano P, Scharlau I, et al. Explanation as a Social Practice:
Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366'
apa: 'Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier,
H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier,
E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth,
H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward
a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366'
bibtex: '@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et
al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework
for the Social Design of AI Systems}, volume={13}, DOI={10.1109/tcds.2020.3044366},
number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems},
author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner,
Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger,
Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021},
pages={717–728} }'
chicago: 'Rohlfing, Katharina J., Philipp Cimiano, Ingrid Scharlau, Tobias Matzner,
Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, et al. “Explanation as a Social
Practice: Toward a Conceptual Framework for the Social Design of AI Systems.”
IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2021):
717–28. https://doi.org/10.1109/tcds.2020.3044366.'
ieee: 'K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021,
doi: 10.1109/tcds.2020.3044366.'
mla: 'Rohlfing, Katharina J., et al. “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems.” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, 2021, pp. 717–28,
doi:10.1109/tcds.2020.3044366.'
short: K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier,
E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier,
F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P.
Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13
(2021) 717–728.
date_created: 2021-09-14T20:52:57Z
date_updated: 2023-12-05T10:15:02Z
ddc:
- '300'
department:
- _id: '603'
- _id: '749'
- _id: '424'
- _id: '67'
- _id: '574'
- _id: '184'
- _id: '757'
- _id: '54'
- _id: '178'
doi: 10.1109/tcds.2020.3044366
file:
- access_level: open_access
content_type: application/pdf
creator: haebumb
date_created: 2023-11-20T16:33:51Z
date_updated: 2023-11-20T16:33:51Z
file_id: '49081'
file_name: 2020-12-01_explainability_final_version.pdf
file_size: 626217
relation: main_file
file_date_updated: 2023-11-20T16:33:51Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '3'
keyword:
- Explainability
- process ofexplaining andunderstanding
- explainable artificial systems
language:
- iso: eng
oa: '1'
page: 717-728
project:
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: IEEE Transactions on Cognitive and Developmental Systems
publication_identifier:
issn:
- 2379-8920
- 2379-8939
publication_status: published
quality_controlled: '1'
status: public
title: 'Explanation as a Social Practice: Toward a Conceptual Framework for the Social
Design of AI Systems'
type: journal_article
user_id: '42933'
volume: 13
year: '2021'
...
---
_id: '17763'
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Haeb-Umbach R. Sprachtechnologien für Digitale Assistenten. In: Böck R, Siegert
I, Wendemuth A, eds. Studientexte Zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung
2020. TUDpress, Dresden; 2020:227-234.'
apa: 'Haeb-Umbach, R. (2020). Sprachtechnologien für Digitale Assistenten. In R.
Böck, I. Siegert, & A. Wendemuth (Eds.), Studientexte zur Sprachkommunikation:
Elektronische Sprachsignalverarbeitung 2020 (pp. 227–234). TUDpress, Dresden.'
bibtex: '@inproceedings{Haeb-Umbach_2020, title={Sprachtechnologien für Digitale
Assistenten}, booktitle={Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung
2020}, publisher={TUDpress, Dresden}, author={Haeb-Umbach, Reinhold}, editor={Böck,
Ronald and Siegert, Ingo and Wendemuth, AndreasEditors}, year={2020}, pages={227–234}
}'
chicago: 'Haeb-Umbach, Reinhold. “Sprachtechnologien Für Digitale Assistenten.”
In Studientexte Zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung
2020, edited by Ronald Böck, Ingo Siegert, and Andreas Wendemuth, 227–34.
TUDpress, Dresden, 2020.'
ieee: 'R. Haeb-Umbach, “Sprachtechnologien für Digitale Assistenten,” in Studientexte
zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020, 2020,
pp. 227–234.'
mla: 'Haeb-Umbach, Reinhold. “Sprachtechnologien Für Digitale Assistenten.” Studientexte
Zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020, edited
by Ronald Böck et al., TUDpress, Dresden, 2020, pp. 227–34.'
short: 'R. Haeb-Umbach, in: R. Böck, I. Siegert, A. Wendemuth (Eds.), Studientexte
Zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2020, TUDpress,
Dresden, 2020, pp. 227–234.'
date_created: 2020-08-10T09:53:12Z
date_updated: 2022-01-06T06:53:19Z
department:
- _id: '54'
editor:
- first_name: Ronald
full_name: Böck, Ronald
last_name: Böck
- first_name: Ingo
full_name: Siegert, Ingo
last_name: Siegert
- first_name: Andreas
full_name: Wendemuth, Andreas
last_name: Wendemuth
keyword:
- Poster
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2020/ESSV_2020_haeb_umbach.pdf
oa: '1'
page: 227-234
publication: 'Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung
2020'
publication_identifier:
isbn:
- 978-3-959081-93-1
publisher: TUDpress, Dresden
status: public
title: Sprachtechnologien für Digitale Assistenten
type: conference
user_id: '44006'
year: '2020'
...
---
_id: '20700'
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Tobias
full_name: Cord-Landwehr, Tobias
id: '44393'
last_name: Cord-Landwehr
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Catalin
full_name: Zorila, Catalin
last_name: Zorila
- first_name: Daichi
full_name: Hayakawa, Daichi
last_name: Hayakawa
- first_name: Mohan
full_name: Li, Mohan
last_name: Li
- first_name: Min
full_name: Liu, Min
last_name: Liu
- first_name: Rama
full_name: Doddipatla, Rama
last_name: Doddipatla
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Cord-Landwehr T, Heitkaemper J, et al. Towards a speaker diarization
system for the CHiME 2020 dinner party transcription. In: Proc. CHiME 2020
Workshop on Speech Processing in Everyday Environments. ; 2020.'
apa: Boeddeker, C., Cord-Landwehr, T., Heitkaemper, J., Zorila, C., Hayakawa, D.,
Li, M., … Haeb-Umbach, R. (2020). Towards a speaker diarization system for the
CHiME 2020 dinner party transcription. In Proc. CHiME 2020 Workshop on Speech
Processing in Everyday Environments.
bibtex: '@inproceedings{Boeddeker_Cord-Landwehr_Heitkaemper_Zorila_Hayakawa_Li_Liu_Doddipatla_Haeb-Umbach_2020,
title={Towards a speaker diarization system for the CHiME 2020 dinner party transcription},
booktitle={Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments},
author={Boeddeker, Christoph and Cord-Landwehr, Tobias and Heitkaemper, Jens and
Zorila, Catalin and Hayakawa, Daichi and Li, Mohan and Liu, Min and Doddipatla,
Rama and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Boeddeker, Christoph, Tobias Cord-Landwehr, Jens Heitkaemper, Catalin Zorila,
Daichi Hayakawa, Mohan Li, Min Liu, Rama Doddipatla, and Reinhold Haeb-Umbach.
“Towards a Speaker Diarization System for the CHiME 2020 Dinner Party Transcription.”
In Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments,
2020.
ieee: C. Boeddeker et al., “Towards a speaker diarization system for the
CHiME 2020 dinner party transcription,” in Proc. CHiME 2020 Workshop on Speech
Processing in Everyday Environments, 2020.
mla: Boeddeker, Christoph, et al. “Towards a Speaker Diarization System for the
CHiME 2020 Dinner Party Transcription.” Proc. CHiME 2020 Workshop on Speech
Processing in Everyday Environments, 2020.
short: 'C. Boeddeker, T. Cord-Landwehr, J. Heitkaemper, C. Zorila, D. Hayakawa,
M. Li, M. Liu, R. Doddipatla, R. Haeb-Umbach, in: Proc. CHiME 2020 Workshop on
Speech Processing in Everyday Environments, 2020.'
date_created: 2020-12-11T12:49:13Z
date_updated: 2022-01-06T06:54:33Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: cbj
date_created: 2020-12-11T12:48:48Z
date_updated: 2020-12-11T12:48:48Z
file_id: '20702'
file_name: template.pdf
file_size: 115421
relation: main_file
file_date_updated: 2020-12-11T12:48:48Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments
status: public
title: Towards a speaker diarization system for the CHiME 2020 dinner party transcription
type: conference
user_id: '40767'
year: '2020'
...
---
_id: '17598'
author:
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Rintaro
full_name: Ikeshita, Rintaro
last_name: Ikeshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Nakatani T, Boeddeker C, Kinoshita K, Ikeshita R, Delcroix M, Haeb-Umbach R.
Jointly optimal denoising, dereverberation, and source separation. IEEE/ACM
Transactions on Audio, Speech, and Language Processing. Published online 2020:1-1.
doi:10.1109/TASLP.2020.3013118
apa: Nakatani, T., Boeddeker, C., Kinoshita, K., Ikeshita, R., Delcroix, M., &
Haeb-Umbach, R. (2020). Jointly optimal denoising, dereverberation, and source
separation. IEEE/ACM Transactions on Audio, Speech, and Language Processing,
1–1. https://doi.org/10.1109/TASLP.2020.3013118
bibtex: '@article{Nakatani_Boeddeker_Kinoshita_Ikeshita_Delcroix_Haeb-Umbach_2020,
title={Jointly optimal denoising, dereverberation, and source separation}, DOI={10.1109/TASLP.2020.3013118},
journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing}, author={Nakatani,
Tomohiro and Boeddeker, Christoph and Kinoshita, Keisuke and Ikeshita, Rintaro
and Delcroix, Marc and Haeb-Umbach, Reinhold}, year={2020}, pages={1–1} }'
chicago: Nakatani, Tomohiro, Christoph Boeddeker, Keisuke Kinoshita, Rintaro Ikeshita,
Marc Delcroix, and Reinhold Haeb-Umbach. “Jointly Optimal Denoising, Dereverberation,
and Source Separation.” IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 2020, 1–1. https://doi.org/10.1109/TASLP.2020.3013118.
ieee: 'T. Nakatani, C. Boeddeker, K. Kinoshita, R. Ikeshita, M. Delcroix, and R.
Haeb-Umbach, “Jointly optimal denoising, dereverberation, and source separation,”
IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1–1,
2020, doi: 10.1109/TASLP.2020.3013118.'
mla: Nakatani, Tomohiro, et al. “Jointly Optimal Denoising, Dereverberation, and
Source Separation.” IEEE/ACM Transactions on Audio, Speech, and Language Processing,
2020, pp. 1–1, doi:10.1109/TASLP.2020.3013118.
short: T. Nakatani, C. Boeddeker, K. Kinoshita, R. Ikeshita, M. Delcroix, R. Haeb-Umbach,
IEEE/ACM Transactions on Audio, Speech, and Language Processing (2020) 1–1.
date_created: 2020-08-05T06:16:56Z
date_updated: 2022-12-05T12:34:01Z
department:
- _id: '54'
doi: 10.1109/TASLP.2020.3013118
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2020/journal_2020_boeddeker.pdf
oa: '1'
page: 1-1
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
status: public
title: Jointly optimal denoising, dereverberation, and source separation
type: journal_article
user_id: '40767'
year: '2020'
...
---
_id: '20504'
abstract:
- lang: eng
text: 'In recent years time domain speech separation has excelled over frequency
domain separation in single channel scenarios and noise-free environments. In
this paper we dissect the gains of the time-domain audio separation network (TasNet)
approach by gradually replacing components of an utterance-level permutation invariant
training (u-PIT) based separation system in the frequency domain until the TasNet
system is reached, thus blending components of frequency domain approaches with
those of time domain approaches. Some of the intermediate variants achieve comparable
signal-to-distortion ratio (SDR) gains to TasNet, but retain the advantage of
frequency domain processing: compatibility with classic signal processing tools
such as frequency-domain beamforming and the human interpretability of the masks.
Furthermore, we show that the scale invariant signal-to-distortion ratio (si-SDR)
criterion used as loss function in TasNet is related to a logarithmic mean square
error criterion and that it is this criterion which contributes most reliable
to the performance advantage of TasNet. Finally, we critically assess which gains
in a noise-free single channel environment generalize to more realistic reverberant
conditions.'
author:
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Darius
full_name: Jakobeit, Darius
last_name: Jakobeit
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Lukas
full_name: Drude, Lukas
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heitkaemper J, Jakobeit D, Boeddeker C, Drude L, Haeb-Umbach R. Demystifying
TasNet: A Dissecting Approach. In: ICASSP 2020 Virtual Barcelona Spain.
; 2020.'
apa: 'Heitkaemper, J., Jakobeit, D., Boeddeker, C., Drude, L., & Haeb-Umbach,
R. (2020). Demystifying TasNet: A Dissecting Approach. ICASSP 2020 Virtual
Barcelona Spain.'
bibtex: '@inproceedings{Heitkaemper_Jakobeit_Boeddeker_Drude_Haeb-Umbach_2020, title={Demystifying
TasNet: A Dissecting Approach}, booktitle={ICASSP 2020 Virtual Barcelona Spain},
author={Heitkaemper, Jens and Jakobeit, Darius and Boeddeker, Christoph and Drude,
Lukas and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: 'Heitkaemper, Jens, Darius Jakobeit, Christoph Boeddeker, Lukas Drude,
and Reinhold Haeb-Umbach. “Demystifying TasNet: A Dissecting Approach.” In ICASSP
2020 Virtual Barcelona Spain, 2020.'
ieee: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, and R. Haeb-Umbach,
“Demystifying TasNet: A Dissecting Approach,” 2020.'
mla: 'Heitkaemper, Jens, et al. “Demystifying TasNet: A Dissecting Approach.” ICASSP
2020 Virtual Barcelona Spain, 2020.'
short: 'J. Heitkaemper, D. Jakobeit, C. Boeddeker, L. Drude, R. Haeb-Umbach, in:
ICASSP 2020 Virtual Barcelona Spain, 2020.'
date_created: 2020-11-25T14:56:53Z
date_updated: 2022-01-13T08:47:32Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: closed
content_type: application/pdf
creator: jensheit
date_created: 2020-12-11T12:36:37Z
date_updated: 2020-12-11T12:36:37Z
file_id: '20699'
file_name: ms.pdf
file_size: 3871374
relation: main_file
success: 1
file_date_updated: 2020-12-11T12:36:37Z
has_accepted_license: '1'
keyword:
- voice activity detection
- speech activity detection
- neural network
- statistical speech processing
language:
- iso: eng
project:
- _id: '52'
name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: ICASSP 2020 Virtual Barcelona Spain
quality_controlled: '1'
status: public
title: 'Demystifying TasNet: A Dissecting Approach'
type: conference
user_id: '40767'
year: '2020'
...
---
_id: '28263'
abstract:
- lang: eng
text: "Following the success of the 1st, 2nd, 3rd, 4th and 5th CHiME challenges
we\r\norganize the 6th CHiME Speech Separation and Recognition Challenge (CHiME-6).\r\nThe
new challenge revisits the previous CHiME-5 challenge and further considers\r\nthe
problem of distant multi-microphone conversational speech diarization and\r\nrecognition
in everyday home environments. Speech material is the same as the\r\nprevious
CHiME-5 recordings except for accurate array synchronization. The\r\nmaterial
was elicited using a dinner party scenario with efforts taken to\r\ncapture data
that is representative of natural conversational speech. This\r\npaper provides
a baseline description of the CHiME-6 challenge for both\r\nsegmented multispeaker
speech recognition (Track 1) and unsegmented\r\nmultispeaker speech recognition
(Track 2). Of note, Track 2 is the first\r\nchallenge activity in the community
to tackle an unsegmented multispeaker\r\nspeech recognition scenario with a complete
set of reproducible open source\r\nbaselines providing speech enhancement, speaker
diarization, and speech\r\nrecognition modules."
author:
- first_name: Shinji
full_name: Watanabe, Shinji
last_name: Watanabe
- first_name: Michael
full_name: Mandel, Michael
last_name: Mandel
- first_name: Jon
full_name: Barker, Jon
last_name: Barker
- first_name: Emmanuel
full_name: Vincent, Emmanuel
last_name: Vincent
- first_name: Ashish
full_name: Arora, Ashish
last_name: Arora
- first_name: Xuankai
full_name: Chang, Xuankai
last_name: Chang
- first_name: Sanjeev
full_name: Khudanpur, Sanjeev
last_name: Khudanpur
- first_name: Vimal
full_name: Manohar, Vimal
last_name: Manohar
- first_name: Daniel
full_name: Povey, Daniel
last_name: Povey
- first_name: Desh
full_name: Raj, Desh
last_name: Raj
- first_name: David
full_name: Snyder, David
last_name: Snyder
- first_name: Aswin Shanmugam
full_name: Subramanian, Aswin Shanmugam
last_name: Subramanian
- first_name: Jan
full_name: Trmal, Jan
last_name: Trmal
- first_name: Bar Ben
full_name: Yair, Bar Ben
last_name: Yair
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Zhaoheng
full_name: Ni, Zhaoheng
last_name: Ni
- first_name: Yusuke
full_name: Fujita, Yusuke
last_name: Fujita
- first_name: Shota
full_name: Horiguchi, Shota
last_name: Horiguchi
- first_name: Naoyuki
full_name: Kanda, Naoyuki
last_name: Kanda
- first_name: Takuya
full_name: Yoshioka, Takuya
last_name: Yoshioka
- first_name: Neville
full_name: Ryant, Neville
last_name: Ryant
citation:
ama: Watanabe S, Mandel M, Barker J, et al. CHiME-6 Challenge:Tackling Multispeaker
Speech Recognition for Unsegmented Recordings. arXiv:200409249. Published
online 2020.
apa: Watanabe, S., Mandel, M., Barker, J., Vincent, E., Arora, A., Chang, X., Khudanpur,
S., Manohar, V., Povey, D., Raj, D., Snyder, D., Subramanian, A. S., Trmal, J.,
Yair, B. B., Boeddeker, C., Ni, Z., Fujita, Y., Horiguchi, S., Kanda, N., … Ryant,
N. (2020). CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented
Recordings. In arXiv:2004.09249.
bibtex: '@article{Watanabe_Mandel_Barker_Vincent_Arora_Chang_Khudanpur_Manohar_Povey_Raj_et
al._2020, title={CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for
Unsegmented Recordings}, journal={arXiv:2004.09249}, author={Watanabe, Shinji
and Mandel, Michael and Barker, Jon and Vincent, Emmanuel and Arora, Ashish and
Chang, Xuankai and Khudanpur, Sanjeev and Manohar, Vimal and Povey, Daniel and
Raj, Desh and et al.}, year={2020} }'
chicago: Watanabe, Shinji, Michael Mandel, Jon Barker, Emmanuel Vincent, Ashish
Arora, Xuankai Chang, Sanjeev Khudanpur, et al. “CHiME-6 Challenge:Tackling Multispeaker
Speech Recognition for Unsegmented Recordings.” ArXiv:2004.09249, 2020.
ieee: S. Watanabe et al., “CHiME-6 Challenge:Tackling Multispeaker Speech
Recognition for Unsegmented Recordings,” arXiv:2004.09249. 2020.
mla: Watanabe, Shinji, et al. “CHiME-6 Challenge:Tackling Multispeaker Speech Recognition
for Unsegmented Recordings.” ArXiv:2004.09249, 2020.
short: S. Watanabe, M. Mandel, J. Barker, E. Vincent, A. Arora, X. Chang, S. Khudanpur,
V. Manohar, D. Povey, D. Raj, D. Snyder, A.S. Subramanian, J. Trmal, B.B. Yair,
C. Boeddeker, Z. Ni, Y. Fujita, S. Horiguchi, N. Kanda, T. Yoshioka, N. Ryant,
ArXiv:2004.09249 (2020).
date_created: 2021-12-03T12:13:01Z
date_updated: 2022-01-13T08:34:37Z
department:
- _id: '54'
language:
- iso: eng
publication: arXiv:2004.09249
status: public
title: CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented
Recordings
type: preprint
user_id: '40767'
year: '2020'
...
---
_id: '20505'
abstract:
- lang: eng
text: "Speech activity detection (SAD), which often rests on the fact that the noise
is \"more'' stationary than speech, is particularly challenging in non-stationary
environments, because the time variance of the acoustic scene makes it difficult
to discriminate speech from noise. We propose two approaches to SAD, where one
is based on statistical signal processing, while the other utilizes neural networks.
The former employs sophisticated signal processing to track the noise and speech
energies and is meant to support the case for a resource efficient, unsupervised
signal processing approach.\r\nThe latter introduces a recurrent network layer
that operates on short segments of the input speech to do temporal smoothing in
the presence of non-stationary noise. The systems are tested on the Fearless Steps
challenge database, which consists of the transmission data from the Apollo-11
space mission.\r\nThe statistical SAD achieves comparable detection performance
to earlier proposed neural network based SADs, while the neural network based
approach leads to a decision cost function of 1.07% on the evaluation set of the
2020 Fearless Steps Challenge, which sets a new state of the art."
author:
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Heitkaemper J, Schmalenstroeer J, Haeb-Umbach R. Statistical and Neural Network
Based Speech Activity Detection in Non-Stationary Acoustic Environments. In: INTERSPEECH
2020 Virtual Shanghai China. ; 2020.'
apa: Heitkaemper, J., Schmalenstroeer, J., & Haeb-Umbach, R. (2020). Statistical
and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic
Environments. INTERSPEECH 2020 Virtual Shanghai China.
bibtex: '@inproceedings{Heitkaemper_Schmalenstroeer_Haeb-Umbach_2020, title={Statistical
and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic
Environments}, booktitle={INTERSPEECH 2020 Virtual Shanghai China}, author={Heitkaemper,
Jens and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Heitkaemper, Jens, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Statistical
and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic
Environments.” In INTERSPEECH 2020 Virtual Shanghai China, 2020.
ieee: J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Statistical and Neural
Network Based Speech Activity Detection in Non-Stationary Acoustic Environments,”
2020.
mla: Heitkaemper, Jens, et al. “Statistical and Neural Network Based Speech Activity
Detection in Non-Stationary Acoustic Environments.” INTERSPEECH 2020 Virtual
Shanghai China, 2020.
short: 'J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: INTERSPEECH 2020
Virtual Shanghai China, 2020.'
date_created: 2020-11-25T15:03:19Z
date_updated: 2023-10-26T08:28:49Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: closed
content_type: application/pdf
creator: jensheit
date_created: 2020-12-11T12:33:04Z
date_updated: 2020-12-11T12:33:04Z
file_id: '20697'
file_name: ms.pdf
file_size: 998706
relation: main_file
success: 1
file_date_updated: 2020-12-11T12:33:04Z
has_accepted_license: '1'
keyword:
- voice activity detection
- speech activity detection
- neural network
- statistical speech processing
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2020 Virtual Shanghai China
status: public
title: Statistical and Neural Network Based Speech Activity Detection in Non-Stationary
Acoustic Environments
type: conference
user_id: '460'
year: '2020'
...
---
_id: '20762'
abstract:
- lang: eng
text: The rising interest in single-channel multi-speaker speech separation sparked
development of End-to-End (E2E) approaches to multispeaker speech recognition.
However, up until now, state-of-theart neural network–based time domain source
separation has not yet been combined with E2E speech recognition. We here demonstrate
how to combine a separation module based on a Convolutional Time domain Audio
Separation Network (Conv-TasNet) with an E2E speech recognizer and how to train
such a model jointly by distributing it over multiple GPUs or by approximating
truncated back-propagation for the convolutional front-end. To put this work into
perspective and illustrate the complexity of the design space, we provide a compact
overview of single-channel multi-speaker recognition systems. Our experiments
show a word error rate of 11.0% on WSJ0-2mix and indicate that our joint time
domain model can yield substantial improvements over cascade DNN-HMM and monolithic
E2E frequency domain systems proposed so far.
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Lukas
full_name: Drude, Lukas
last_name: Drude
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- 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: 'von Neumann T, Kinoshita K, Drude L, et al. End-to-End Training of Time Domain
Audio Separation and Recognition. In: ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). ; 2020:7004-7008.
doi:10.1109/ICASSP40776.2020.9053461'
apa: von Neumann, T., Kinoshita, K., Drude, L., Boeddeker, C., Delcroix, M., Nakatani,
T., & Haeb-Umbach, R. (2020). End-to-End Training of Time Domain Audio Separation
and Recognition. ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 7004–7008. https://doi.org/10.1109/ICASSP40776.2020.9053461
bibtex: '@inproceedings{von Neumann_Kinoshita_Drude_Boeddeker_Delcroix_Nakatani_Haeb-Umbach_2020,
title={End-to-End Training of Time Domain Audio Separation and Recognition}, DOI={10.1109/ICASSP40776.2020.9053461},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)}, author={von Neumann, Thilo and Kinoshita, Keisuke
and Drude, Lukas and Boeddeker, Christoph and Delcroix, Marc and Nakatani, Tomohiro
and Haeb-Umbach, Reinhold}, year={2020}, pages={7004–7008} }'
chicago: Neumann, Thilo von, Keisuke Kinoshita, Lukas Drude, Christoph Boeddeker,
Marc Delcroix, Tomohiro Nakatani, and Reinhold Haeb-Umbach. “End-to-End Training
of Time Domain Audio Separation and Recognition.” In ICASSP 2020 - 2020 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
7004–8, 2020. https://doi.org/10.1109/ICASSP40776.2020.9053461.
ieee: 'T. von Neumann et al., “End-to-End Training of Time Domain Audio Separation
and Recognition,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2020, pp. 7004–7008, doi: 10.1109/ICASSP40776.2020.9053461.'
mla: von Neumann, Thilo, et al. “End-to-End Training of Time Domain Audio Separation
and Recognition.” ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2020, pp. 7004–08, doi:10.1109/ICASSP40776.2020.9053461.
short: 'T. von Neumann, K. Kinoshita, L. Drude, C. Boeddeker, M. Delcroix, T. Nakatani,
R. Haeb-Umbach, in: ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP), 2020, pp. 7004–7008.'
date_created: 2020-12-16T14:07:54Z
date_updated: 2023-11-15T12:17:45Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/ICASSP40776.2020.9053461
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T14:09:48Z
date_updated: 2020-12-16T14:09:48Z
file_id: '20763'
file_name: ICASSP_2020_vonNeumann_Paper.pdf
file_size: 192529
relation: main_file
file_date_updated: 2020-12-16T14:09:48Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 7004-7008
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
quality_controlled: '1'
status: public
title: End-to-End Training of Time Domain Audio Separation and Recognition
type: conference
user_id: '49870'
year: '2020'
...
---
_id: '20764'
abstract:
- lang: eng
text: 'Most approaches to multi-talker overlapped speech separation and recognition
assume that the number of simultaneously active speakers is given, but in realistic
situations, it is typically unknown. To cope with this, we extend an iterative
speech extraction system with mechanisms to count the number of sources and combine
it with a single-talker speech recognizer to form the first end-to-end multi-talker
automatic speech recognition system for an unknown number of active speakers.
Our experiments show very promising performance in counting accuracy, source separation
and speech recognition on simulated clean mixtures from WSJ0-2mix and WSJ0-3mix.
Among others, we set a new state-of-the-art word error rate on the WSJ0-2mix database.
Furthermore, our system generalizes well to a larger number of speakers than it
ever saw during training, as shown in experiments with the WSJ0-4mix database. '
author:
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Lukas
full_name: Drude, Lukas
last_name: Drude
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- 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: 'von Neumann T, Boeddeker C, Drude L, et al. Multi-Talker ASR for an Unknown
Number of Sources: Joint Training of Source Counting, Separation and ASR. In:
Proc. Interspeech 2020. ; 2020:3097-3101. doi:10.21437/Interspeech.2020-2519'
apa: 'von Neumann, T., Boeddeker, C., Drude, L., Kinoshita, K., Delcroix, M., Nakatani,
T., & Haeb-Umbach, R. (2020). Multi-Talker ASR for an Unknown Number of Sources:
Joint Training of Source Counting, Separation and ASR. Proc. Interspeech 2020,
3097–3101. https://doi.org/10.21437/Interspeech.2020-2519'
bibtex: '@inproceedings{von Neumann_Boeddeker_Drude_Kinoshita_Delcroix_Nakatani_Haeb-Umbach_2020,
title={Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source
Counting, Separation and ASR}, DOI={10.21437/Interspeech.2020-2519},
booktitle={Proc. Interspeech 2020}, author={von Neumann, Thilo and Boeddeker,
Christoph and Drude, Lukas and Kinoshita, Keisuke and Delcroix, Marc and Nakatani,
Tomohiro and Haeb-Umbach, Reinhold}, year={2020}, pages={3097–3101} }'
chicago: 'Neumann, Thilo von, Christoph Boeddeker, Lukas Drude, Keisuke Kinoshita,
Marc Delcroix, Tomohiro Nakatani, and Reinhold Haeb-Umbach. “Multi-Talker ASR
for an Unknown Number of Sources: Joint Training of Source Counting, Separation
and ASR.” In Proc. Interspeech 2020, 3097–3101, 2020. https://doi.org/10.21437/Interspeech.2020-2519.'
ieee: 'T. von Neumann et al., “Multi-Talker ASR for an Unknown Number of
Sources: Joint Training of Source Counting, Separation and ASR,” in Proc. Interspeech
2020, 2020, pp. 3097–3101, doi: 10.21437/Interspeech.2020-2519.'
mla: 'von Neumann, Thilo, et al. “Multi-Talker ASR for an Unknown Number of Sources:
Joint Training of Source Counting, Separation and ASR.” Proc. Interspeech 2020,
2020, pp. 3097–101, doi:10.21437/Interspeech.2020-2519.'
short: 'T. von Neumann, C. Boeddeker, L. Drude, K. Kinoshita, M. Delcroix, T. Nakatani,
R. Haeb-Umbach, in: Proc. Interspeech 2020, 2020, pp. 3097–3101.'
date_created: 2020-12-16T14:12:45Z
date_updated: 2023-11-15T12:17:57Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.21437/Interspeech.2020-2519
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T14:14:14Z
date_updated: 2020-12-16T14:14:14Z
file_id: '20765'
file_name: INTERSPEECH_2020_vonNeumann_Paper.pdf
file_size: 267893
relation: main_file
file_date_updated: 2020-12-16T14:14:14Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 3097-3101
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. Interspeech 2020
quality_controlled: '1'
status: public
title: 'Multi-Talker ASR for an Unknown Number of Sources: Joint Training of Source
Counting, Separation and ASR'
type: conference
user_id: '49870'
year: '2020'
...
---
_id: '18651'
abstract:
- lang: eng
text: 'We present an approach to deep neural network based (DNN-based) distance
estimation in reverberant rooms for supporting geometry calibration tasks in wireless
acoustic sensor networks. Signal diffuseness information from acoustic signals
is aggregated via the coherent-to-diffuse power ratio to obtain a distance-related
feature, which is mapped to a source-to-microphone distance estimate by means
of a DNN. This information is then combined with direction-of-arrival estimates
from compact microphone arrays to infer the geometry of the sensor network. Unlike
many other approaches to geometry calibration, the proposed scheme does only require
that the sampling clocks of the sensor nodes are roughly synchronized. In simulations
we show that the proposed DNN-based distance estimator generalizes to unseen acoustic
environments and that precise estimates of the sensor node positions are obtained. '
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Andreas
full_name: Brendel, Andreas
last_name: Brendel
- first_name: Walter
full_name: Kellermann, Walter
last_name: Kellermann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Gburrek T, Schmalenstroeer J, Brendel A, Kellermann W, Haeb-Umbach R. Deep
Neural Network based Distance Estimation for Geometry Calibration in Acoustic
Sensor Network. In: European Signal Processing Conference (EUSIPCO). ;
2020.'
apa: Gburrek, T., Schmalenstroeer, J., Brendel, A., Kellermann, W., & Haeb-Umbach,
R. (2020). Deep Neural Network based Distance Estimation for Geometry Calibration
in Acoustic Sensor Network. European Signal Processing Conference (EUSIPCO).
bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Brendel_Kellermann_Haeb-Umbach_2020,
title={Deep Neural Network based Distance Estimation for Geometry Calibration
in Acoustic Sensor Network}, booktitle={European Signal Processing Conference
(EUSIPCO)}, author={Gburrek, Tobias and Schmalenstroeer, Joerg and Brendel, Andreas
and Kellermann, Walter and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Gburrek, Tobias, Joerg Schmalenstroeer, Andreas Brendel, Walter Kellermann,
and Reinhold Haeb-Umbach. “Deep Neural Network Based Distance Estimation for Geometry
Calibration in Acoustic Sensor Network.” In European Signal Processing Conference
(EUSIPCO), 2020.
ieee: T. Gburrek, J. Schmalenstroeer, A. Brendel, W. Kellermann, and R. Haeb-Umbach,
“Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic
Sensor Network,” 2020.
mla: Gburrek, Tobias, et al. “Deep Neural Network Based Distance Estimation for
Geometry Calibration in Acoustic Sensor Network.” European Signal Processing
Conference (EUSIPCO), 2020.
short: 'T. Gburrek, J. Schmalenstroeer, A. Brendel, W. Kellermann, R. Haeb-Umbach,
in: European Signal Processing Conference (EUSIPCO), 2020.'
date_created: 2020-08-31T07:20:57Z
date_updated: 2023-11-17T06:23:39Z
ddc:
- '004'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: tgburrek
date_created: 2023-11-17T06:21:40Z
date_updated: 2023-11-17T06:21:40Z
file_id: '48987'
file_name: Gburrek2020.pdf
file_size: 292159
relation: main_file
file_date_updated: 2023-11-17T06:21:40Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
publication: European Signal Processing Conference (EUSIPCO)
quality_controlled: '1'
status: public
title: Deep Neural Network based Distance Estimation for Geometry Calibration in Acoustic
Sensor Network
type: conference
user_id: '44006'
year: '2020'
...
---
_id: '20766'
abstract:
- lang: eng
text: Recently, the source separation performance was greatly improved by time-domain
audio source separation based on dual-path recurrent neural network (DPRNN). DPRNN
is a simple but effective model for a long sequential data. While DPRNN is quite
efficient in modeling a sequential data of the length of an utterance, i.e., about
5 to 10 second data, it is harder to apply it to longer sequences such as whole
conversations consisting of multiple utterances. It is simply because, in such
a case, the number of time steps consumed by its internal module called inter-chunk
RNN becomes extremely large. To mitigate this problem, this paper proposes a multi-path
RNN (MPRNN), a generalized version of DPRNN, that models the input data in a hierarchical
manner. In the MPRNN framework, the input data is represented at several (>_ 3)
time-resolutions, each of which is modeled by a specific RNN sub-module. For example,
the RNN sub-module that deals with the finest resolution may model temporal relationship
only within a phoneme, while the RNN sub-module handling the most coarse resolution
may capture only the relationship between utterances such as speaker information.
We perform experiments using simulated dialogue-like mixtures and show that MPRNN
has greater model capacity, and it outperforms the current state-of-the-art DPRNN
framework especially in online processing scenarios.
author:
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Thilo
full_name: von Neumann, Thilo
id: '49870'
last_name: von Neumann
orcid: https://orcid.org/0000-0002-7717-8670
- first_name: Marc
full_name: Delcroix, Marc
last_name: Delcroix
- 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: 'Kinoshita K, von Neumann T, Delcroix M, Nakatani T, Haeb-Umbach R. Multi-Path
RNN for Hierarchical Modeling of Long Sequential Data and its Application to Speaker
Stream Separation. In: Proc. Interspeech 2020. ; 2020:2652-2656. doi:10.21437/Interspeech.2020-2388'
apa: Kinoshita, K., von Neumann, T., Delcroix, M., Nakatani, T., & Haeb-Umbach,
R. (2020). Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and
its Application to Speaker Stream Separation. Proc. Interspeech 2020, 2652–2656.
https://doi.org/10.21437/Interspeech.2020-2388
bibtex: '@inproceedings{Kinoshita_von Neumann_Delcroix_Nakatani_Haeb-Umbach_2020,
title={Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its
Application to Speaker Stream Separation}, DOI={10.21437/Interspeech.2020-2388},
booktitle={Proc. Interspeech 2020}, author={Kinoshita, Keisuke and von Neumann,
Thilo and Delcroix, Marc and Nakatani, Tomohiro and Haeb-Umbach, Reinhold}, year={2020},
pages={2652–2656} }'
chicago: Kinoshita, Keisuke, Thilo von Neumann, Marc Delcroix, Tomohiro Nakatani,
and Reinhold Haeb-Umbach. “Multi-Path RNN for Hierarchical Modeling of Long Sequential
Data and Its Application to Speaker Stream Separation.” In Proc. Interspeech
2020, 2652–56, 2020. https://doi.org/10.21437/Interspeech.2020-2388.
ieee: 'K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, and R. Haeb-Umbach,
“Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application
to Speaker Stream Separation,” in Proc. Interspeech 2020, 2020, pp. 2652–2656,
doi: 10.21437/Interspeech.2020-2388.'
mla: Kinoshita, Keisuke, et al. “Multi-Path RNN for Hierarchical Modeling of Long
Sequential Data and Its Application to Speaker Stream Separation.” Proc. Interspeech
2020, 2020, pp. 2652–56, doi:10.21437/Interspeech.2020-2388.
short: 'K. Kinoshita, T. von Neumann, M. Delcroix, T. Nakatani, R. Haeb-Umbach,
in: Proc. Interspeech 2020, 2020, pp. 2652–2656.'
date_created: 2020-12-16T14:15:24Z
date_updated: 2023-11-15T12:14:25Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.21437/Interspeech.2020-2388
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T14:16:32Z
date_updated: 2020-12-16T14:16:32Z
file_id: '20767'
file_name: INTERSPEECH_2020_vonNeumann1_Paper.pdf
file_size: 1725219
relation: main_file
file_date_updated: 2020-12-16T14:16:32Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 2652-2656
publication: Proc. Interspeech 2020
quality_controlled: '1'
status: public
title: Multi-Path RNN for Hierarchical Modeling of Long Sequential Data and its Application
to Speaker Stream Separation
type: conference
user_id: '49870'
year: '2020'
...