---
_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: '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'
...
---
_id: '20753'
abstract:
- lang: eng
text: 'In this paper we present our system for the detection and classification
of acoustic scenes and events (DCASE) 2020 Challenge Task 4: Sound event detection
and separation in domestic environments. We introduce two new models: the forward-backward
convolutional recurrent neural network (FBCRNN) and the tag-conditioned convolutional
neural network (CNN). The FBCRNN employs two recurrent neural network (RNN) classifiers
sharing the same CNN for preprocessing. With one RNN processing a recording in
forward direction and the other in backward direction, the two networks are trained
to jointly predict audio tags, i.e., weak labels, at each time step within a recording,
given that at each time step they have jointly processed the whole recording.
The proposed training encourages the classifiers to tag events as soon as possible.
Therefore, after training, the networks can be applied to shorter audio segments
of, e.g., 200ms, allowing sound event detection (SED). Further, we propose a tag-conditioned
CNN to complement SED. It is trained to predict strong labels while using (predicted)
tags, i.e., weak labels, as additional input. For training pseudo strong labels
from a FBCRNN ensemble are used. The presented system scored the fourth and third
place in the systems and teams rankings, respectively. Subsequent improvements
allow our system to even outperform the challenge baseline and winner systems
in average by, respectively, 18.0% and 2.2% event-based F1-score on the validation
set. 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. Forward-Backward Convolutional Recurrent Neural Networks
and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled Semi-Supervised
Sound Event Detection. In: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020). ; 2020.'
apa: Ebbers, J., & Haeb-Umbach, R. (2020). Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection. Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020).
bibtex: '@inproceedings{Ebbers_Haeb-Umbach_2020, title={Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection}, booktitle={Proceedings
of the Detection and Classification of Acoustic Scenes and Events 2020 Workshop
(DCASE2020)}, author={Ebbers, Janek and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional
Recurrent Neural Networks and Tag-Conditioned Convolutional Neural Networks for
Weakly Labeled Semi-Supervised Sound Event Detection.” In Proceedings of the
Detection and Classification of Acoustic Scenes and Events 2020 Workshop (DCASE2020),
2020.
ieee: J. Ebbers and R. Haeb-Umbach, “Forward-Backward Convolutional Recurrent Neural
Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection,” 2020.
mla: Ebbers, Janek, and Reinhold Haeb-Umbach. “Forward-Backward Convolutional Recurrent
Neural Networks and Tag-Conditioned Convolutional Neural Networks for Weakly Labeled
Semi-Supervised Sound Event Detection.” Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.
short: 'J. Ebbers, R. Haeb-Umbach, in: Proceedings of the Detection and Classification
of Acoustic Scenes and Events 2020 Workshop (DCASE2020), 2020.'
date_created: 2020-12-16T08:55:27Z
date_updated: 2023-11-22T08:27:32Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-12-16T08:57:22Z
date_updated: 2020-12-16T08:57:22Z
file_id: '20754'
file_name: DCASE2020Workshop_Ebbers_Paper.pdf
file_size: 108326
relation: main_file
file_date_updated: 2020-12-16T08:57:22Z
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: Proceedings of the Detection and Classification of Acoustic Scenes and
Events 2020 Workshop (DCASE2020)
quality_controlled: '1'
status: public
title: Forward-Backward Convolutional Recurrent Neural Networks and Tag-Conditioned
Convolutional Neural Networks for Weakly Labeled Semi-Supervised Sound Event Detection
type: conference
user_id: '34851'
year: '2020'
...
---
_id: '20695'
author:
- first_name: Christoph
full_name: Boeddeker, Christoph
id: '40767'
last_name: Boeddeker
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Boeddeker C, Nakatani T, Kinoshita K, Haeb-Umbach R. Jointly Optimal Dereverberation
and Beamforming. In: ICASSP 2020 - 2020 IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP). ; 2020. doi:10.1109/icassp40776.2020.9054393'
apa: Boeddeker, C., Nakatani, T., Kinoshita, K., & Haeb-Umbach, R. (2020). Jointly
Optimal Dereverberation and Beamforming. ICASSP 2020 - 2020 IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp40776.2020.9054393
bibtex: '@inproceedings{Boeddeker_Nakatani_Kinoshita_Haeb-Umbach_2020, title={Jointly
Optimal Dereverberation and Beamforming}, DOI={10.1109/icassp40776.2020.9054393},
booktitle={ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)}, author={Boeddeker, Christoph and Nakatani, Tomohiro
and Kinoshita, Keisuke and Haeb-Umbach, Reinhold}, year={2020} }'
chicago: Boeddeker, Christoph, Tomohiro Nakatani, Keisuke Kinoshita, and Reinhold
Haeb-Umbach. “Jointly Optimal Dereverberation and Beamforming.” In ICASSP 2020
- 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2020. https://doi.org/10.1109/icassp40776.2020.9054393.
ieee: 'C. Boeddeker, T. Nakatani, K. Kinoshita, and R. Haeb-Umbach, “Jointly Optimal
Dereverberation and Beamforming,” 2020, doi: 10.1109/icassp40776.2020.9054393.'
mla: Boeddeker, Christoph, et al. “Jointly Optimal Dereverberation and Beamforming.”
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP), 2020, doi:10.1109/icassp40776.2020.9054393.
short: 'C. Boeddeker, T. Nakatani, K. Kinoshita, R. Haeb-Umbach, in: ICASSP 2020
- 2020 IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP), 2020.'
date_created: 2020-12-11T12:28:49Z
date_updated: 2024-11-14T09:17:32Z
ddc:
- '000'
department:
- _id: '54'
doi: 10.1109/icassp40776.2020.9054393
file:
- access_level: open_access
content_type: application/pdf
creator: cbj
date_created: 2020-12-11T12:32:44Z
date_updated: 2020-12-11T12:32:44Z
file_id: '20698'
file_name: convBF.pdf
file_size: 200127
relation: main_file
file_date_updated: 2020-12-11T12:32:44Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP)
publication_identifier:
isbn:
- '9781509066315'
publication_status: published
status: public
title: Jointly Optimal Dereverberation and Beamforming
type: conference
user_id: '40767'
year: '2020'
...
---
_id: '17762'
abstract:
- lang: eng
text: 'Abstract Wenn akustische Signalverarbeitung mit automatisiertem Lernen verknüpft
wird: Nachrichtentechniker arbeiten mit mehreren Mikrofonen und tiefen neuronalen
Netzen an besserer Spracherkennung unter widrigsten Bedingungen. Von solchen Sensornetzwerken
könnten langfristig auch digitale Sprachassistenten profitieren.'
author:
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Haeb-Umbach R. Lektionen für Alexa \& Co?! forschung. 2019;44(1):12-15.
doi:10.1002/fors.201970104
apa: Haeb-Umbach, R. (2019). Lektionen für Alexa \& Co?! Forschung, 44(1),
12–15. https://doi.org/10.1002/fors.201970104
bibtex: '@article{Haeb-Umbach_2019, title={Lektionen für Alexa \& Co?!}, volume={44},
DOI={10.1002/fors.201970104},
number={1}, journal={forschung}, author={Haeb-Umbach, Reinhold}, year={2019},
pages={12–15} }'
chicago: 'Haeb-Umbach, Reinhold. “Lektionen Für Alexa \& Co?!” Forschung
44, no. 1 (2019): 12–15. https://doi.org/10.1002/fors.201970104.'
ieee: R. Haeb-Umbach, “Lektionen für Alexa \& Co?!,” forschung, vol.
44, no. 1, pp. 12–15, 2019.
mla: Haeb-Umbach, Reinhold. “Lektionen Für Alexa \& Co?!” Forschung,
vol. 44, no. 1, 2019, pp. 12–15, doi:10.1002/fors.201970104.
short: R. Haeb-Umbach, Forschung 44 (2019) 12–15.
date_created: 2020-08-10T09:51:09Z
date_updated: 2022-01-06T06:53:19Z
department:
- _id: '54'
doi: 10.1002/fors.201970104
intvolume: ' 44'
issue: '1'
language:
- iso: eng
page: 12-15
publication: forschung
status: public
title: Lektionen für Alexa \& Co?!
type: journal_article
user_id: '44006'
volume: 44
year: '2019'
...
---
_id: '19446'
abstract:
- lang: eng
text: 'We present a multi-channel database of overlapping speech for training, evaluation,
and detailed analysis of source separation and extraction algorithms: SMS-WSJ
-- Spatialized Multi-Speaker Wall Street Journal. It consists of artificially
mixed speech taken from the WSJ database, but unlike earlier databases we consider
all WSJ0+1 utterances and take care of strictly separating the speaker sets present
in the training, validation and test sets. When spatializing the data we ensure
a high degree of randomness w.r.t. room size, array center and rotation, as well
as speaker position. Furthermore, this paper offers a critical assessment of recently
proposed measures of source separation performance. Alongside the code to generate
the database we provide a source separation baseline and a Kaldi recipe with competitive
word error rates to provide common ground for evaluation.'
author:
- first_name: Lukas
full_name: Drude, Lukas
last_name: Drude
- first_name: Jens
full_name: Heitkaemper, Jens
id: '27643'
last_name: Heitkaemper
- 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, Heitkaemper J, Boeddeker C, Haeb-Umbach R. SMS-WSJ: Database, performance
measures, and baseline recipe for multi-channel source separation and recognition.
ArXiv e-prints. 2019.'
apa: 'Drude, L., Heitkaemper, J., Boeddeker, C., & Haeb-Umbach, R. (2019). SMS-WSJ:
Database, performance measures, and baseline recipe for multi-channel source separation
and recognition. ArXiv E-Prints.'
bibtex: '@article{Drude_Heitkaemper_Boeddeker_Haeb-Umbach_2019, title={SMS-WSJ:
Database, performance measures, and baseline recipe for multi-channel source separation
and recognition}, journal={ArXiv e-prints}, author={Drude, Lukas and Heitkaemper,
Jens and Boeddeker, Christoph and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: 'Drude, Lukas, Jens Heitkaemper, Christoph Boeddeker, and Reinhold Haeb-Umbach.
“SMS-WSJ: Database, Performance Measures, and Baseline Recipe for Multi-Channel
Source Separation and Recognition.” ArXiv E-Prints, 2019.'
ieee: 'L. Drude, J. Heitkaemper, C. Boeddeker, and R. Haeb-Umbach, “SMS-WSJ: Database,
performance measures, and baseline recipe for multi-channel source separation
and recognition,” ArXiv e-prints, 2019.'
mla: 'Drude, Lukas, et al. “SMS-WSJ: Database, Performance Measures, and Baseline
Recipe for Multi-Channel Source Separation and Recognition.” ArXiv E-Prints,
2019.'
short: L. Drude, J. Heitkaemper, C. Boeddeker, R. Haeb-Umbach, ArXiv E-Prints (2019).
date_created: 2020-09-16T07:59:46Z
date_updated: 2022-01-06T06:54:04Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2020-09-16T08:00:56Z
date_updated: 2020-12-11T12:22:31Z
file_id: '19448'
file_name: ArXiv_2019_Drude.pdf
file_size: 288594
relation: main_file
file_date_updated: 2020-12-11T12:22:31Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ArXiv e-prints
status: public
title: 'SMS-WSJ: Database, performance measures, and baseline recipe for multi-channel
source separation and recognition'
type: journal_article
user_id: '40767'
year: '2019'
...
---
_id: '11965'
abstract:
- lang: eng
text: 'We present an unsupervised training approach for a neural network-based mask
estimator in an acoustic beamforming application. The network is trained to maximize
a likelihood criterion derived from a spatial mixture model of the observations.
It is trained from scratch without requiring any parallel data consisting of degraded
input and clean training targets. Thus, training can be carried out on real recordings
of noisy speech rather than simulated ones. In contrast to previous work on unsupervised
training of neural mask estimators, our approach avoids the need for a possibly
pre-trained teacher model entirely. We demonstrate the effectiveness of our approach
by speech recognition experiments on two different datasets: one mainly deteriorated
by noise (CHiME 4) and one by reverberation (REVERB). The results show that the
performance of the proposed system is on par with a supervised system using oracle
target masks for training and with a system trained using a model-based teacher.'
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Heymann J, Haeb-Umbach R. Unsupervised training of neural mask-based
beamforming. In: INTERSPEECH 2019, Graz, Austria. ; 2019.'
apa: Drude, L., Heymann, J., & Haeb-Umbach, R. (2019). Unsupervised training
of neural mask-based beamforming. In INTERSPEECH 2019, Graz, Austria.
bibtex: '@inproceedings{Drude_Heymann_Haeb-Umbach_2019, title={Unsupervised training
of neural mask-based beamforming}, booktitle={INTERSPEECH 2019, Graz, Austria},
author={Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2019}
}'
chicago: Drude, Lukas, Jahn Heymann, and Reinhold Haeb-Umbach. “Unsupervised Training
of Neural Mask-Based Beamforming.” In INTERSPEECH 2019, Graz, Austria,
2019.
ieee: L. Drude, J. Heymann, and R. Haeb-Umbach, “Unsupervised training of neural
mask-based beamforming,” in INTERSPEECH 2019, Graz, Austria, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of Neural Mask-Based Beamforming.”
INTERSPEECH 2019, Graz, Austria, 2019.
short: 'L. Drude, J. Heymann, R. Haeb-Umbach, in: INTERSPEECH 2019, Graz, Austria,
2019.'
date_created: 2019-07-18T09:11:39Z
date_updated: 2022-01-06T06:51:14Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-13T06:36:44Z
date_updated: 2019-08-13T06:41:35Z
file_id: '12914'
file_name: INTERSPEECH_2019_Drude_Paper.pdf
file_size: 223413
relation: main_file
file_date_updated: 2019-08-13T06:41:35Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: INTERSPEECH 2019, Graz, Austria
status: public
title: Unsupervised training of neural mask-based beamforming
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12874'
abstract:
- lang: eng
text: We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar performance
as the multi-channel teacher in terms of word error rates and (b) initializing
the spatial clustering approach with the deep clustering result yields a relative
word error rate reduction of 26% over the unsupervised teacher.
author:
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Daniel
full_name: Hasenklever, Daniel
last_name: Hasenklever
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Drude L, Hasenklever D, Haeb-Umbach R. Unsupervised Training of a Deep Clustering
Model for Multichannel Blind Source Separation. In: ICASSP 2019, Brighton,
UK. ; 2019.'
apa: Drude, L., Hasenklever, D., & Haeb-Umbach, R. (2019). Unsupervised Training
of a Deep Clustering Model for Multichannel Blind Source Separation. In ICASSP
2019, Brighton, UK.
bibtex: '@inproceedings{Drude_Hasenklever_Haeb-Umbach_2019, title={Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation},
booktitle={ICASSP 2019, Brighton, UK}, author={Drude, Lukas and Hasenklever, Daniel
and Haeb-Umbach, Reinhold}, year={2019} }'
chicago: Drude, Lukas, Daniel Hasenklever, and Reinhold Haeb-Umbach. “Unsupervised
Training of a Deep Clustering Model for Multichannel Blind Source Separation.”
In ICASSP 2019, Brighton, UK, 2019.
ieee: L. Drude, D. Hasenklever, and R. Haeb-Umbach, “Unsupervised Training of a
Deep Clustering Model for Multichannel Blind Source Separation,” in ICASSP
2019, Brighton, UK, 2019.
mla: Drude, Lukas, et al. “Unsupervised Training of a Deep Clustering Model for
Multichannel Blind Source Separation.” ICASSP 2019, Brighton, UK, 2019.
short: 'L. Drude, D. Hasenklever, R. Haeb-Umbach, in: ICASSP 2019, Brighton, UK,
2019.'
date_created: 2019-07-23T07:37:54Z
date_updated: 2022-01-06T06:51:21Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-08-14T07:19:13Z
date_updated: 2019-08-14T07:19:13Z
file_id: '12925'
file_name: ICASSP_2019_Drude_Paper.pdf
file_size: 368225
relation: main_file
file_date_updated: 2019-08-14T07:19:13Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Unsupervised Training of a Deep Clustering Model for Multichannel Blind Source
Separation
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '12875'
abstract:
- lang: eng
text: Signal dereverberation using the Weighted Prediction Error (WPE) method has
been proven to be an effective means to raise the accuracy of far-field speech
recognition. First proposed as an iterative algorithm, follow-up works have reformulated
it as a recursive least squares algorithm and therefore enabled its use in online
applications. For this algorithm, the estimation of the power spectral density
(PSD) of the anechoic signal plays an important role and strongly influences its
performance. Recently, we showed that using a neural network PSD estimator leads
to improved performance for online automatic speech recognition. This, however,
comes at a price. To train the network, we require parallel data, i.e., utterances
simultaneously available in clean and reverberated form. Here we propose to overcome
this limitation by training the network jointly with the acoustic model of the
speech recognizer. To be specific, the gradients computed from the cross-entropy
loss between the target senone sequence and the acoustic model network output
is backpropagated through the complex-valued dereverberation filter estimation
to the neural network for PSD estimation. Evaluation on two databases demonstrates
improved performance for on-line processing scenarios while imposing fewer requirements
on the available training data and thus widening the range of applications.
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Lukas
full_name: Drude, Lukas
id: '11213'
last_name: Drude
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Keisuke
full_name: Kinoshita, Keisuke
last_name: Kinoshita
- first_name: Tomohiro
full_name: Nakatani, Tomohiro
last_name: Nakatani
citation:
ama: 'Heymann J, Drude L, Haeb-Umbach R, Kinoshita K, Nakatani T. Joint Optimization
of Neural Network-based WPE Dereverberation and Acoustic Model for Robust Online
ASR. In: ICASSP 2019, Brighton, UK. ; 2019.'
apa: Heymann, J., Drude, L., Haeb-Umbach, R., Kinoshita, K., & Nakatani, T.
(2019). Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR. In ICASSP 2019, Brighton, UK.
bibtex: '@inproceedings{Heymann_Drude_Haeb-Umbach_Kinoshita_Nakatani_2019, title={Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR}, booktitle={ICASSP 2019, Brighton, UK}, author={Heymann, Jahn
and Drude, Lukas and Haeb-Umbach, Reinhold and Kinoshita, Keisuke and Nakatani,
Tomohiro}, year={2019} }'
chicago: Heymann, Jahn, Lukas Drude, Reinhold Haeb-Umbach, Keisuke Kinoshita, and
Tomohiro Nakatani. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” In ICASSP 2019, Brighton, UK,
2019.
ieee: J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, and T. Nakatani, “Joint
Optimization of Neural Network-based WPE Dereverberation and Acoustic Model for
Robust Online ASR,” in ICASSP 2019, Brighton, UK, 2019.
mla: Heymann, Jahn, et al. “Joint Optimization of Neural Network-Based WPE Dereverberation
and Acoustic Model for Robust Online ASR.” ICASSP 2019, Brighton, UK, 2019.
short: 'J. Heymann, L. Drude, R. Haeb-Umbach, K. Kinoshita, T. Nakatani, in: ICASSP
2019, Brighton, UK, 2019.'
date_created: 2019-07-23T07:42:26Z
date_updated: 2022-01-06T06:51:22Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: huesera
date_created: 2019-12-17T07:28:06Z
date_updated: 2019-12-17T07:28:06Z
file_id: '15334'
file_name: ICASSP_2019_Heymann_Paper.pdf
file_size: 199109
relation: main_file
file_date_updated: 2019-12-17T07:28:06Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ICASSP 2019, Brighton, UK
status: public
title: Joint Optimization of Neural Network-based WPE Dereverberation and Acoustic
Model for Robust Online ASR
type: conference
user_id: '59789'
year: '2019'
...