---
_id: '33471'
abstract:
- lang: eng
  text: "The intelligibility of demodulated audio signals from analog high frequency
    transmissions, e.g., using single-sideband\r\n(SSB) modulation, can be severely
    degraded by channel distortions and/or a mismatch between modulation and demodulation
    carrier frequency. In this work a neural network (NN)-based approach for carrier
    frequency offset (CFO) estimation from demodulated SSB signals is proposed, whereby
    a task specific architecture is presented. Additionally, a simulation framework
    for SSB signals is introduced and utilized for training the NNs. The CFO estimator
    is combined with a speech enhancement network to investigate its influence on
    the enhancement performance. The NN-based system is compared to a recently proposed
    pitch tracking based approach on publicly available data from real high frequency
    transmissions. Experiments show that the NN exhibits good CFO estimation properties
    and results in significant improvements in speech intelligibility, especially
    when combined with a noise reduction network."
author:
- first_name: Jens
  full_name: Heitkämper, Jens
  id: '27643'
  last_name: Heitkämper
- 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: 'Heitkämper J, Schmalenstroeer J, Haeb-Umbach R. Neural Network Based Carrier
    Frequency Offset Estimation From Speech Transmitted Over High Frequency Channels.
    In: <i>Proceedings of the 30th European Signal Processing Conference (EUSIPCO)</i>.'
  apa: Heitkämper, J., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (n.d.). Neural Network
    Based Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency
    Channels. <i>Proceedings of the 30th European Signal Processing Conference (EUSIPCO)</i>.
    30th European Signal Processing Conference (EUSIPCO), Belgrad.
  bibtex: '@inproceedings{Heitkämper_Schmalenstroeer_Haeb-Umbach, place={Belgrad},
    title={Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted
    Over High Frequency Channels}, booktitle={Proceedings of the 30th European Signal
    Processing Conference (EUSIPCO)}, author={Heitkämper, Jens and Schmalenstroeer,
    Joerg and Haeb-Umbach, Reinhold} }'
  chicago: Heitkämper, Jens, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Neural
    Network Based Carrier Frequency Offset Estimation From Speech Transmitted Over
    High Frequency Channels.” In <i>Proceedings of the 30th European Signal Processing
    Conference (EUSIPCO)</i>. Belgrad, n.d.
  ieee: J. Heitkämper, J. Schmalenstroeer, and R. Haeb-Umbach, “Neural Network Based
    Carrier Frequency Offset Estimation From Speech Transmitted Over High Frequency
    Channels,” presented at the 30th European Signal Processing Conference (EUSIPCO),
    Belgrad.
  mla: Heitkämper, Jens, et al. “Neural Network Based Carrier Frequency Offset Estimation
    From Speech Transmitted Over High Frequency Channels.” <i>Proceedings of the 30th
    European Signal Processing Conference (EUSIPCO)</i>.
  short: 'J. Heitkämper, J. Schmalenstroeer, R. Haeb-Umbach, in: Proceedings of the
    30th European Signal Processing Conference (EUSIPCO), Belgrad, n.d.'
conference:
  end_date: 2022-09-02
  location: Belgrad
  name: 30th European Signal Processing Conference (EUSIPCO)
  start_date: 2022-08-29
date_created: 2022-09-22T10:56:13Z
date_updated: 2023-10-26T08:15:57Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: closed
  content_type: application/pdf
  creator: jensheit
  date_created: 2022-09-22T10:48:31Z
  date_updated: 2022-09-22T10:48:31Z
  file_id: '33472'
  file_name: cfo.pdf
  file_size: 1231379
  relation: main_file
  success: 1
file_date_updated: 2022-09-22T10:48:31Z
has_accepted_license: '1'
language:
- iso: eng
place: Belgrad
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Proceedings of the 30th European Signal Processing Conference (EUSIPCO)
publication_status: accepted
quality_controlled: '1'
status: public
title: Neural Network Based Carrier Frequency Offset Estimation From Speech Transmitted
  Over High Frequency Channels
type: conference
user_id: '460'
year: '2022'
...
---
_id: '33808'
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: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Gburrek T, Schmalenstroeer J, Heitkaemper J, Haeb-Umbach R. Informed vs. Blind
    Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription. In:
    <i>2022 International Workshop on Acoustic Signal Enhancement (IWAENC)</i>. IEEE;
    2022. doi:<a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">10.1109/IWAENC53105.2022.9914772</a>'
  apa: Gburrek, T., Schmalenstroeer, J., Heitkaemper, J., &#38; Haeb-Umbach, R. (2022).
    Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting
    Transcription. <i>2022 International Workshop on Acoustic Signal Enhancement (IWAENC)</i>.
    17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022),  Bamberg,
    Germany . <a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">https://doi.org/10.1109/IWAENC53105.2022.9914772</a>
  bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Heitkaemper_Haeb-Umbach_2022, title={Informed
    vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription},
    DOI={<a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">10.1109/IWAENC53105.2022.9914772</a>},
    booktitle={2022 International Workshop on Acoustic Signal Enhancement (IWAENC)},
    publisher={IEEE}, author={Gburrek, Tobias and Schmalenstroeer, Joerg and Heitkaemper,
    Jens and Haeb-Umbach, Reinhold}, year={2022} }'
  chicago: Gburrek, Tobias, Joerg Schmalenstroeer, Jens Heitkaemper, and Reinhold
    Haeb-Umbach. “Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks
    for Meeting Transcription.” In <i>2022 International Workshop on Acoustic Signal
    Enhancement (IWAENC)</i>. IEEE, 2022. <a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">https://doi.org/10.1109/IWAENC53105.2022.9914772</a>.
  ieee: 'T. Gburrek, J. Schmalenstroeer, J. Heitkaemper, and R. Haeb-Umbach, “Informed
    vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting Transcription,”
    presented at the 17th International Workshop on Acoustic Signal Enhancement (IWAENC
    2022),  Bamberg, Germany , 2022, doi: <a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">10.1109/IWAENC53105.2022.9914772</a>.'
  mla: Gburrek, Tobias, et al. “Informed vs. Blind Beamforming in Ad-Hoc Acoustic
    Sensor Networks for Meeting Transcription.” <i>2022 International Workshop on
    Acoustic Signal Enhancement (IWAENC)</i>, IEEE, 2022, doi:<a href="https://doi.org/10.1109/IWAENC53105.2022.9914772">10.1109/IWAENC53105.2022.9914772</a>.
  short: 'T. Gburrek, J. Schmalenstroeer, J. Heitkaemper, R. Haeb-Umbach, in: 2022
    International Workshop on Acoustic Signal Enhancement (IWAENC), IEEE, 2022.'
conference:
  end_date: 2022-09-08
  location: ' Bamberg, Germany '
  name: 17th International Workshop on Acoustic Signal Enhancement (IWAENC 2022)
  start_date: 2022-09-05
date_created: 2022-10-18T09:30:24Z
date_updated: 2023-11-17T06:40:58Z
ddc:
- '004'
department:
- _id: '54'
doi: 10.1109/IWAENC53105.2022.9914772
file:
- access_level: open_access
  content_type: application/pdf
  creator: tgburrek
  date_created: 2023-11-17T06:40:40Z
  date_updated: 2023-11-17T06:40:40Z
  file_id: '48991'
  file_name: iwaenc_22_camera_ready_ieee_check.pdf
  file_size: 266475
  relation: main_file
file_date_updated: 2023-11-17T06:40:40Z
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: 2022 International Workshop on Acoustic Signal Enhancement (IWAENC)
publisher: IEEE
quality_controlled: '1'
status: public
title: Informed vs. Blind Beamforming in Ad-Hoc Acoustic Sensor Networks for Meeting
  Transcription
type: conference
user_id: '44006'
year: '2022'
...
---
_id: '24000'
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: Valentin
  full_name: Ion, Valentin
  last_name: Ion
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Heitkaemper J, Schmalenstroeer J, Ion V, Haeb-Umbach R. A Database for Research
    on Detection and Enhancement of Speech Transmitted over HF links. In: <i>Speech
    Communication; 14th ITG-Symposium</i>. ; 2021:1-5.'
  apa: Heitkaemper, J., Schmalenstroeer, J., Ion, V., &#38; Haeb-Umbach, R. (2021).
    A Database for Research on Detection and Enhancement of Speech Transmitted over
    HF links. <i>Speech Communication; 14th ITG-Symposium</i>, 1–5.
  bibtex: '@inproceedings{Heitkaemper_Schmalenstroeer_Ion_Haeb-Umbach_2021, title={A
    Database for Research on Detection and Enhancement of Speech Transmitted over
    HF links}, booktitle={Speech Communication; 14th ITG-Symposium}, author={Heitkaemper,
    Jens and Schmalenstroeer, Joerg and Ion, Valentin and Haeb-Umbach, Reinhold},
    year={2021}, pages={1–5} }'
  chicago: Heitkaemper, Jens, Joerg Schmalenstroeer, Valentin Ion, and Reinhold Haeb-Umbach.
    “A Database for Research on Detection and Enhancement of Speech Transmitted over
    HF Links.” In <i>Speech Communication; 14th ITG-Symposium</i>, 1–5, 2021.
  ieee: J. Heitkaemper, J. Schmalenstroeer, V. Ion, and R. Haeb-Umbach, “A Database
    for Research on Detection and Enhancement of Speech Transmitted over HF links,”
    in <i>Speech Communication; 14th ITG-Symposium</i>, 2021, pp. 1–5.
  mla: Heitkaemper, Jens, et al. “A Database for Research on Detection and Enhancement
    of Speech Transmitted over HF Links.” <i>Speech Communication; 14th ITG-Symposium</i>,
    2021, pp. 1–5.
  short: 'J. Heitkaemper, J. Schmalenstroeer, V. Ion, R. Haeb-Umbach, in: Speech Communication;
    14th ITG-Symposium, 2021, pp. 1–5.'
date_created: 2021-09-09T08:41:25Z
date_updated: 2023-10-26T08:06:57Z
department:
- _id: '54'
language:
- iso: eng
page: 1-5
project:
- _id: '52'
  name: 'PC2: Computing Resources Provided by the Paderborn Center for Parallel Computing'
publication: Speech Communication; 14th ITG-Symposium
quality_controlled: '1'
status: public
title: A Database for Research on Detection and Enhancement of Speech Transmitted
  over HF links
type: conference
user_id: '460'
year: '2021'
...
---
_id: '23998'
author:
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Joerg
  full_name: Ullmann, Joerg
  id: '16256'
  last_name: Ullmann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Schmalenstroeer J, Heitkaemper J, Ullmann J, Haeb-Umbach R. Open Range Pitch
    Tracking for Carrier Frequency Difference Estimation from HF Transmitted Speech.
    In: <i>29th European Signal Processing Conference (EUSIPCO)</i>. ; 2021:1-5.'
  apa: Schmalenstroeer, J., Heitkaemper, J., Ullmann, J., &#38; Haeb-Umbach, R. (2021).
    Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF
    Transmitted Speech. <i>29th European Signal Processing Conference (EUSIPCO)</i>,
    1–5.
  bibtex: '@inproceedings{Schmalenstroeer_Heitkaemper_Ullmann_Haeb-Umbach_2021, title={Open
    Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted
    Speech}, booktitle={29th European Signal Processing Conference (EUSIPCO)}, author={Schmalenstroeer,
    Joerg and Heitkaemper, Jens and Ullmann, Joerg and Haeb-Umbach, Reinhold}, year={2021},
    pages={1–5} }'
  chicago: Schmalenstroeer, Joerg, Jens Heitkaemper, Joerg Ullmann, and Reinhold Haeb-Umbach.
    “Open Range Pitch Tracking for Carrier Frequency Difference Estimation from HF
    Transmitted Speech.” In <i>29th European Signal Processing Conference (EUSIPCO)</i>,
    1–5, 2021.
  ieee: J. Schmalenstroeer, J. Heitkaemper, J. Ullmann, and R. Haeb-Umbach, “Open
    Range Pitch Tracking for Carrier Frequency Difference Estimation from HF Transmitted
    Speech,” in <i>29th European Signal Processing Conference (EUSIPCO)</i>, 2021,
    pp. 1–5.
  mla: Schmalenstroeer, Joerg, et al. “Open Range Pitch Tracking for Carrier Frequency
    Difference Estimation from HF Transmitted Speech.” <i>29th European Signal Processing
    Conference (EUSIPCO)</i>, 2021, pp. 1–5.
  short: 'J. Schmalenstroeer, J. Heitkaemper, J. Ullmann, R. Haeb-Umbach, in: 29th
    European Signal Processing Conference (EUSIPCO), 2021, pp. 1–5.'
date_created: 2021-09-09T08:40:04Z
date_updated: 2023-11-15T14:56:38Z
department:
- _id: '54'
extern: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2103.01599
oa: '1'
page: 1-5
publication: 29th European Signal Processing Conference (EUSIPCO)
status: public
title: Open Range Pitch Tracking for Carrier Frequency Difference Estimation from
  HF Transmitted Speech
type: conference
user_id: '460'
year: '2021'
...
---
_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: <i>Proc. CHiME 2020
    Workshop on Speech Processing in Everyday Environments</i>. ; 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 <i>Proc. CHiME 2020 Workshop on Speech
    Processing in Everyday Environments</i>.
  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 <i>Proc. CHiME 2020 Workshop on Speech Processing in Everyday Environments</i>,
    2020.
  ieee: C. Boeddeker <i>et al.</i>, “Towards a speaker diarization system for the
    CHiME 2020 dinner party transcription,” in <i>Proc. CHiME 2020 Workshop on Speech
    Processing in Everyday Environments</i>, 2020.
  mla: Boeddeker, Christoph, et al. “Towards a Speaker Diarization System for the
    CHiME 2020 Dinner Party Transcription.” <i>Proc. CHiME 2020 Workshop on Speech
    Processing in Everyday Environments</i>, 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: '20701'
abstract:
- lang: eng
  text: This paper describes Asteroid , the PyTorch -based audio source separation
    toolkit for researchers. Inspired by the most successful neural source separation
    systems, it provides all neural building blocks required to build such a system.
    To improve reproducibility, Kaldi-style recipes on common audio source separation
    datasets are also provided. This paper describes the software architecture of
    Asteroid and its most important features. By showing experimental results obtained
    with Asteroid ’s recipes, we show that our implementations are at least on par
    with most results reported in reference papers. The toolkit is publicly available
    at github.com/mpariente/asteroid.
author:
- first_name: Manuel
  full_name: Pariente, Manuel
  last_name: Pariente
- first_name: Samuele
  full_name: Cornell, Samuele
  last_name: Cornell
- first_name: Joris
  full_name: Cosentino, Joris
  last_name: Cosentino
- first_name: Sunit
  full_name: Sivasankaran, Sunit
  last_name: Sivasankaran
- first_name: Efthymios
  full_name: Tzinis, Efthymios
  last_name: Tzinis
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Michel
  full_name: Olvera, Michel
  last_name: Olvera
- first_name: Fabian-Robert
  full_name: Stöter, Fabian-Robert
  last_name: Stöter
- first_name: Mathieu
  full_name: Hu, Mathieu
  last_name: Hu
- first_name: Juan M.
  full_name: Martín-Doñas, Juan M.
  last_name: Martín-Doñas
- first_name: David
  full_name: Ditter, David
  last_name: Ditter
- first_name: Ariel
  full_name: Frank, Ariel
  last_name: Frank
- first_name: Antoine
  full_name: Deleforge, Antoine
  last_name: Deleforge
- first_name: Emmanuel
  full_name: Vincent, Emmanuel
  last_name: Vincent
citation:
  ama: 'Pariente M, Cornell S, Cosentino J, et al. Asteroid: The PyTorch-Based Audio
    Source Separation Toolkit for Researchers. In: <i>Interspeech 2020</i>. ; 2020.
    doi:<a href="https://doi.org/10.21437/interspeech.2020-1673">10.21437/interspeech.2020-1673</a>'
  apa: 'Pariente, M., Cornell, S., Cosentino, J., Sivasankaran, S., Tzinis, E., Heitkaemper,
    J., … Vincent, E. (2020). Asteroid: The PyTorch-Based Audio Source Separation
    Toolkit for Researchers. In <i>Interspeech 2020</i>. <a href="https://doi.org/10.21437/interspeech.2020-1673">https://doi.org/10.21437/interspeech.2020-1673</a>'
  bibtex: '@inproceedings{Pariente_Cornell_Cosentino_Sivasankaran_Tzinis_Heitkaemper_Olvera_Stöter_Hu_Martín-Doñas_et
    al._2020, title={Asteroid: The PyTorch-Based Audio Source Separation Toolkit for
    Researchers}, DOI={<a href="https://doi.org/10.21437/interspeech.2020-1673">10.21437/interspeech.2020-1673</a>},
    booktitle={Interspeech 2020}, author={Pariente, Manuel and Cornell, Samuele and
    Cosentino, Joris and Sivasankaran, Sunit and Tzinis, Efthymios and Heitkaemper,
    Jens and Olvera, Michel and Stöter, Fabian-Robert and Hu, Mathieu and Martín-Doñas,
    Juan M. and et al.}, year={2020} }'
  chicago: 'Pariente, Manuel, Samuele Cornell, Joris Cosentino, Sunit Sivasankaran,
    Efthymios Tzinis, Jens Heitkaemper, Michel Olvera, et al. “Asteroid: The PyTorch-Based
    Audio Source Separation Toolkit for Researchers.” In <i>Interspeech 2020</i>,
    2020. <a href="https://doi.org/10.21437/interspeech.2020-1673">https://doi.org/10.21437/interspeech.2020-1673</a>.'
  ieee: 'M. Pariente <i>et al.</i>, “Asteroid: The PyTorch-Based Audio Source Separation
    Toolkit for Researchers,” in <i>Interspeech 2020</i>, 2020.'
  mla: 'Pariente, Manuel, et al. “Asteroid: The PyTorch-Based Audio Source Separation
    Toolkit for Researchers.” <i>Interspeech 2020</i>, 2020, doi:<a href="https://doi.org/10.21437/interspeech.2020-1673">10.21437/interspeech.2020-1673</a>.'
  short: 'M. Pariente, S. Cornell, J. Cosentino, S. Sivasankaran, E. Tzinis, J. Heitkaemper,
    M. Olvera, F.-R. Stöter, M. Hu, J.M. Martín-Doñas, D. Ditter, A. Frank, A. Deleforge,
    E. Vincent, in: Interspeech 2020, 2020.'
date_created: 2020-12-11T12:46:16Z
date_updated: 2022-01-06T06:54:33Z
doi: 10.21437/interspeech.2020-1673
language:
- iso: eng
popular_science: '1'
publication: Interspeech 2020
publication_status: published
status: public
title: 'Asteroid: The PyTorch-Based Audio Source Separation Toolkit for Researchers'
type: conference
user_id: '27643'
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: <i>ICASSP 2020 Virtual Barcelona Spain</i>.
    ; 2020.'
  apa: 'Heitkaemper, J., Jakobeit, D., Boeddeker, C., Drude, L., &#38; Haeb-Umbach,
    R. (2020). Demystifying TasNet: A Dissecting Approach. <i>ICASSP 2020 Virtual
    Barcelona Spain</i>.'
  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 <i>ICASSP
    2020 Virtual Barcelona Spain</i>, 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.” <i>ICASSP
    2020 Virtual Barcelona Spain</i>, 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: <i>INTERSPEECH
    2020 Virtual Shanghai China</i>. ; 2020.'
  apa: Heitkaemper, J., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2020). Statistical
    and Neural Network Based Speech Activity Detection in Non-Stationary Acoustic
    Environments. <i>INTERSPEECH 2020 Virtual Shanghai China</i>.
  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 <i>INTERSPEECH 2020 Virtual Shanghai China</i>, 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.” <i>INTERSPEECH 2020 Virtual
    Shanghai China</i>, 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: '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.
    <i>ArXiv e-prints</i>. 2019.'
  apa: 'Drude, L., Heitkaemper, J., Boeddeker, C., &#38; Haeb-Umbach, R. (2019). SMS-WSJ:
    Database, performance measures, and baseline recipe for multi-channel source separation
    and recognition. <i>ArXiv E-Prints</i>.'
  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.” <i>ArXiv E-Prints</i>, 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,” <i>ArXiv e-prints</i>, 2019.'
  mla: 'Drude, Lukas, et al. “SMS-WSJ: Database, Performance Measures, and Baseline
    Recipe for Multi-Channel Source Separation and Recognition.” <i>ArXiv E-Prints</i>,
    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: '14822'
abstract:
- lang: eng
  text: Multi-talker speech and moving speakers still pose a significant challenge
    to automatic speech recognition systems. Assuming an enrollment utterance of the
    target speakeris available, the so-called SpeakerBeam concept has been recently
    proposed to extract the target speaker from a speech mixture. If multi-channel
    input is available, spatial properties of the speaker can be exploited to support
    the source extraction. In this contribution we investigate different approaches
    to exploit such spatial information. In particular, we are interested in the question,
    how useful this information is if the target speaker changes his/her position.
    To this end, we present a SpeakerBeam-based source extraction network that is
    adapted to work on moving speakers by recursively updating the beamformer coefficients.
    Experimental results are presented on two data sets, one with articially created
    room impulse responses, and one with real room impulse responses and noise recorded
    in a conference room. Interestingly, spatial features turn out to be advantageous
    even if the speaker position changes.
author:
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Thomas
  full_name: Feher, Thomas
  last_name: Feher
- first_name: Michael
  full_name: Freitag, Michael
  last_name: Freitag
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Heitkaemper J, Feher T, Freitag M, Haeb-Umbach R. A Study on Online Source
    Extraction in the Presence of Changing Speaker Positions. In: <i>International
    Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia</i>.
    ; 2019.'
  apa: Heitkaemper, J., Feher, T., Freitag, M., &#38; Haeb-Umbach, R. (2019). A Study
    on Online Source Extraction in the Presence of Changing Speaker Positions. In
    <i>International Conference on Statistical Language and Speech Processing 2019,
    Ljubljana, Slovenia</i>.
  bibtex: '@inproceedings{Heitkaemper_Feher_Freitag_Haeb-Umbach_2019, title={A Study
    on Online Source Extraction in the Presence of Changing Speaker Positions}, booktitle={International
    Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia},
    author={Heitkaemper, Jens and Feher, Thomas and Freitag, Michael and Haeb-Umbach,
    Reinhold}, year={2019} }'
  chicago: Heitkaemper, Jens, Thomas Feher, Michael Freitag, and Reinhold Haeb-Umbach.
    “A Study on Online Source Extraction in the Presence of Changing Speaker Positions.”
    In <i>International Conference on Statistical Language and Speech Processing 2019,
    Ljubljana, Slovenia</i>, 2019.
  ieee: J. Heitkaemper, T. Feher, M. Freitag, and R. Haeb-Umbach, “A Study on Online
    Source Extraction in the Presence of Changing Speaker Positions,” in <i>International
    Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia</i>,
    2019.
  mla: Heitkaemper, Jens, et al. “A Study on Online Source Extraction in the Presence
    of Changing Speaker Positions.” <i>International Conference on Statistical Language
    and Speech Processing 2019, Ljubljana, Slovenia</i>, 2019.
  short: 'J. Heitkaemper, T. Feher, M. Freitag, R. Haeb-Umbach, in: International
    Conference on Statistical Language and Speech Processing 2019, Ljubljana, Slovenia,
    2019.'
date_created: 2019-11-06T09:43:03Z
date_updated: 2022-01-06T06:52:06Z
ddc:
- '006'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2019-11-06T10:02:26Z
  date_updated: 2019-11-08T07:47:12Z
  file_id: '14823'
  file_name: SLSP_2019_Heitkaemper_Paper.pdf
  file_size: 578595
  relation: main_file
file_date_updated: 2019-11-08T07:47:12Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: International Conference on Statistical Language and Speech Processing
  2019, Ljubljana, Slovenia
status: public
title: A Study on Online Source Extraction in the Presence of Changing Speaker Positions
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '14824'
abstract:
- lang: eng
  text: This paper deals with multi-channel speech recognition in scenarios with multiple
    speakers. Recently, the spectral characteristics of a target speaker, extracted
    from an adaptation utterance, have been used to guide a neural network mask estimator
    to focus on that speaker. In this work we present two variants of speakeraware
    neural networks, which exploit both spectral and spatial information to allow
    better discrimination between target and interfering speakers. Thus, we introduce
    either a spatial preprocessing prior to the mask estimation or a spatial plus
    spectral speaker characterization block whose output is directly fed into the
    neural mask estimator. The target speaker’s spectral and spatial signature is
    extracted from an adaptation utterance recorded at the beginning of a session.
    We further adapt the architecture for low-latency processing by means of block-online
    beamforming that recursively updates the signal statistics. Experimental results
    show that the additional spatial information clearly improves source extraction,
    in particular in the same-gender case, and that our proposal achieves state-of-the-art
    performance in terms of distortion reduction and recognition accuracy.
author:
- first_name: Juan M.
  full_name: Martin-Donas, Juan M.
  last_name: Martin-Donas
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Angel M.
  full_name: Gomez, Angel M.
  last_name: Gomez
- first_name: Antonio M.
  full_name: Peinado, Antonio M.
  last_name: Peinado
citation:
  ama: 'Martin-Donas JM, Heitkaemper J, Haeb-Umbach R, Gomez AM, Peinado AM. Multi-Channel
    Block-Online Source Extraction based on Utterance Adaptation. In: <i>INTERSPEECH
    2019, Graz, Austria</i>. ; 2019.'
  apa: Martin-Donas, J. M., Heitkaemper, J., Haeb-Umbach, R., Gomez, A. M., &#38;
    Peinado, A. M. (2019). Multi-Channel Block-Online Source Extraction based on Utterance
    Adaptation. In <i>INTERSPEECH 2019, Graz, Austria</i>.
  bibtex: '@inproceedings{Martin-Donas_Heitkaemper_Haeb-Umbach_Gomez_Peinado_2019,
    title={Multi-Channel Block-Online Source Extraction based on Utterance Adaptation},
    booktitle={INTERSPEECH 2019, Graz, Austria}, author={Martin-Donas, Juan M. and
    Heitkaemper, Jens and Haeb-Umbach, Reinhold and Gomez, Angel M. and Peinado, Antonio
    M.}, year={2019} }'
  chicago: Martin-Donas, Juan M., Jens Heitkaemper, Reinhold Haeb-Umbach, Angel M.
    Gomez, and Antonio M. Peinado. “Multi-Channel Block-Online Source Extraction Based
    on Utterance Adaptation.” In <i>INTERSPEECH 2019, Graz, Austria</i>, 2019.
  ieee: J. M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A. M. Gomez, and A. M.
    Peinado, “Multi-Channel Block-Online Source Extraction based on Utterance Adaptation,”
    in <i>INTERSPEECH 2019, Graz, Austria</i>, 2019.
  mla: Martin-Donas, Juan M., et al. “Multi-Channel Block-Online Source Extraction
    Based on Utterance Adaptation.” <i>INTERSPEECH 2019, Graz, Austria</i>, 2019.
  short: 'J.M. Martin-Donas, J. Heitkaemper, R. Haeb-Umbach, A.M. Gomez, A.M. Peinado,
    in: INTERSPEECH 2019, Graz, Austria, 2019.'
date_created: 2019-11-06T10:04:49Z
date_updated: 2022-01-06T06:52:07Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2019-11-06T10:07:15Z
  date_updated: 2019-11-08T07:46:37Z
  file_id: '14825'
  file_name: INTERSPEECH_2019_Heitkaemper_Paper.pdf
  file_size: 225689
  relation: main_file
file_date_updated: 2019-11-08T07:46:37Z
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: Multi-Channel Block-Online Source Extraction based on Utterance Adaptation
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '14826'
abstract:
- lang: eng
  text: In this paper, we present Hitachi and Paderborn University’s joint effort
    for automatic speech recognition (ASR) in a dinner party scenario. The main challenges
    of ASR systems for dinner party recordings obtained by multiple microphone arrays
    are (1) heavy speech overlaps, (2) severe noise and reverberation, (3) very natural
    onversational content, and possibly (4) insufficient training data. As an example
    of a dinner party scenario, we have chosen the data presented during the CHiME-5
    speech recognition challenge, where the baseline ASR had a 73.3% word error rate
    (WER), and even the best performing system at the CHiME-5 challenge had a 46.1%
    WER. We extensively investigated a combination of the guided source separation-based
    speech enhancement technique and an already proposed strong ASR backend and found
    that a tight combination of these techniques provided substantial accuracy improvements.
    Our final system achieved WERs of 39.94% and 41.64% for the development and evaluation
    data, respectively, both of which are the best published results for the dataset.
    We also investigated with additional training data on the official small data
    in the CHiME-5 corpus to assess the intrinsic difficulty of this ASR task.
author:
- first_name: Naoyuki
  full_name: Kanda, Naoyuki
  last_name: Kanda
- first_name: Christoph
  full_name: Boeddeker, Christoph
  id: '40767'
  last_name: Boeddeker
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Yusuke
  full_name: Fujita, Yusuke
  last_name: Fujita
- first_name: Shota
  full_name: Horiguchi, Shota
  last_name: Horiguchi
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Kanda N, Boeddeker C, Heitkaemper J, Fujita Y, Horiguchi S, Haeb-Umbach R.
    Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
    Joint Investigation for Dinner Party ASR. In: <i>INTERSPEECH 2019, Graz, Austria</i>.
    ; 2019.'
  apa: 'Kanda, N., Boeddeker, C., Heitkaemper, J., Fujita, Y., Horiguchi, S., &#38;
    Haeb-Umbach, R. (2019). Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn
    University Joint Investigation for Dinner Party ASR. In <i>INTERSPEECH 2019, Graz,
    Austria</i>.'
  bibtex: '@inproceedings{Kanda_Boeddeker_Heitkaemper_Fujita_Horiguchi_Haeb-Umbach_2019,
    title={Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn
    University Joint Investigation for Dinner Party ASR}, booktitle={INTERSPEECH 2019,
    Graz, Austria}, author={Kanda, Naoyuki and Boeddeker, Christoph and Heitkaemper,
    Jens and Fujita, Yusuke and Horiguchi, Shota and Haeb-Umbach, Reinhold}, year={2019}
    }'
  chicago: 'Kanda, Naoyuki, Christoph Boeddeker, Jens Heitkaemper, Yusuke Fujita,
    Shota Horiguchi, and Reinhold Haeb-Umbach. “Guided Source Separation Meets a Strong
    ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party
    ASR.” In <i>INTERSPEECH 2019, Graz, Austria</i>, 2019.'
  ieee: 'N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, and R. Haeb-Umbach,
    “Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
    Joint Investigation for Dinner Party ASR,” in <i>INTERSPEECH 2019, Graz, Austria</i>,
    2019.'
  mla: 'Kanda, Naoyuki, et al. “Guided Source Separation Meets a Strong ASR Backend:
    Hitachi/Paderborn University Joint Investigation for Dinner Party ASR.” <i>INTERSPEECH
    2019, Graz, Austria</i>, 2019.'
  short: 'N. Kanda, C. Boeddeker, J. Heitkaemper, Y. Fujita, S. Horiguchi, R. Haeb-Umbach,
    in: INTERSPEECH 2019, Graz, Austria, 2019.'
date_created: 2019-11-06T10:08:49Z
date_updated: 2022-01-06T06:52:07Z
ddc:
- '000'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: huesera
  date_created: 2019-11-06T10:10:23Z
  date_updated: 2019-11-08T07:45:15Z
  file_id: '14827'
  file_name: INTERSPEECH_2019_Boeddeker_Paper.pdf
  file_size: 216202
  relation: main_file
file_date_updated: 2019-11-08T07:45:15Z
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: 'Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University
  Joint Investigation for Dinner Party ASR'
type: conference
user_id: '59789'
year: '2019'
...
---
_id: '11837'
abstract:
- lang: eng
  text: We present a block-online multi-channel front end for automatic speech recognition
    in noisy and reverberated environments. It is an online version of our earlier
    proposed neural network supported acoustic beamformer, whose coefficients are
    calculated from noise and speech spatial covariance matrices which are estimated
    utilizing a neural mask estimator. However, the sparsity of speech in the STFT
    domain causes problems for the initial beamformer coefficients estimation in some
    frequency bins due to lack of speech observations. We propose two methods to mitigate
    this issue. The first is to lower the frequency resolution of the STFT, which
    comes with the additional advantage of a reduced time window, thus lowering the
    latency introduced by block processing. The second approach is to smooth beamforming
    coefficients along the frequency axis, thus exploiting their high interfrequency
    correlation. With both approaches the gap between offline and block-online beamformer
    performance, as measured by the word error rate achieved by a downstream speech
    recognizer, is significantly reduced. Experiments are carried out on two copora,
    representing noisy (CHiME-4) and noisy reverberant (voiceHome) environments.
author:
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- 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: 'Heitkaemper J, Heymann J, Haeb-Umbach R. Smoothing along Frequency in Online
    Neural Network Supported Acoustic Beamforming. In: <i>ITG 2018, Oldenburg, Germany</i>.
    ; 2018.'
  apa: Heitkaemper, J., Heymann, J., &#38; Haeb-Umbach, R. (2018). Smoothing along
    Frequency in Online Neural Network Supported Acoustic Beamforming. In <i>ITG 2018,
    Oldenburg, Germany</i>.
  bibtex: '@inproceedings{Heitkaemper_Heymann_Haeb-Umbach_2018, title={Smoothing along
    Frequency in Online Neural Network Supported Acoustic Beamforming}, booktitle={ITG
    2018, Oldenburg, Germany}, author={Heitkaemper, Jens and Heymann, Jahn and Haeb-Umbach,
    Reinhold}, year={2018} }'
  chicago: Heitkaemper, Jens, Jahn Heymann, and Reinhold Haeb-Umbach. “Smoothing along
    Frequency in Online Neural Network Supported Acoustic Beamforming.” In <i>ITG
    2018, Oldenburg, Germany</i>, 2018.
  ieee: J. Heitkaemper, J. Heymann, and R. Haeb-Umbach, “Smoothing along Frequency
    in Online Neural Network Supported Acoustic Beamforming,” in <i>ITG 2018, Oldenburg,
    Germany</i>, 2018.
  mla: Heitkaemper, Jens, et al. “Smoothing along Frequency in Online Neural Network
    Supported Acoustic Beamforming.” <i>ITG 2018, Oldenburg, Germany</i>, 2018.
  short: 'J. Heitkaemper, J. Heymann, R. Haeb-Umbach, in: ITG 2018, Oldenburg, Germany,
    2018.'
date_created: 2019-07-12T05:29:13Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Heitkaemper_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
related_material:
  link:
  - description: Slides
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Heitkaemper_Slides.pdf
status: public
title: Smoothing along Frequency in Online Neural Network Supported Acoustic Beamforming
type: conference
user_id: '44006'
year: '2018'
...
---
_id: '12899'
abstract:
- lang: eng
  text: This contribution presents a speech enhancement system for the CHiME-5 Dinner
    Party Scenario. The front-end employs multi-channel linear time-variant filtering
    and achieves its gains without the use of a neural network. We present an adaptation
    of blind source separation techniques to the CHiME-5 database which we call Guided
    Source Separation (GSS). Using the baseline acoustic and language model, the combination
    of Weighted Prediction Error based dereverberation, guided source separation,
    and beamforming reduces the WER by 10:54% (relative) for the single array track
    and by 21:12% (relative) on the multiple array track.
author:
- first_name: Christoph
  full_name: Boeddeker, Christoph
  id: '40767'
  last_name: Boeddeker
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Jahn
  full_name: Heymann, Jahn
  last_name: Heymann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Boeddeker C, Heitkaemper J, Schmalenstroeer J, Drude L, Heymann J, Haeb-Umbach
    R. Front-End Processing for the CHiME-5 Dinner Party Scenario. In: <i>Proc. CHiME
    2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India</i>.
    ; 2018.'
  apa: Boeddeker, C., Heitkaemper, J., Schmalenstroeer, J., Drude, L., Heymann, J.,
    &#38; Haeb-Umbach, R. (2018). Front-End Processing for the CHiME-5 Dinner Party
    Scenario. <i>Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
    Hyderabad, India</i>.
  bibtex: '@inproceedings{Boeddeker_Heitkaemper_Schmalenstroeer_Drude_Heymann_Haeb-Umbach_2018,
    title={Front-End Processing for the CHiME-5 Dinner Party Scenario}, booktitle={Proc.
    CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
    India}, author={Boeddeker, Christoph and Heitkaemper, Jens and Schmalenstroeer,
    Joerg and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}, year={2018}
    }'
  chicago: Boeddeker, Christoph, Jens Heitkaemper, Joerg Schmalenstroeer, Lukas Drude,
    Jahn Heymann, and Reinhold Haeb-Umbach. “Front-End Processing for the CHiME-5
    Dinner Party Scenario.” In <i>Proc. CHiME 2018 Workshop on Speech Processing in
    Everyday Environments, Hyderabad, India</i>, 2018.
  ieee: C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann, and
    R. Haeb-Umbach, “Front-End Processing for the CHiME-5 Dinner Party Scenario,”
    2018.
  mla: Boeddeker, Christoph, et al. “Front-End Processing for the CHiME-5 Dinner Party
    Scenario.” <i>Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
    Hyderabad, India</i>, 2018.
  short: 'C. Boeddeker, J. Heitkaemper, J. Schmalenstroeer, L. Drude, J. Heymann,
    R. Haeb-Umbach, in: Proc. CHiME 2018 Workshop on Speech Processing in Everyday
    Environments, Hyderabad, India, 2018.'
date_created: 2019-07-30T14:35:15Z
date_updated: 2023-10-26T08:14:15Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Paper.pdf
oa: '1'
project:
- _id: '52'
  name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
  Hyderabad, India
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_Poster.pdf
status: public
title: Front-End Processing for the CHiME-5 Dinner Party Scenario
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11876'
abstract:
- lang: eng
  text: This paper describes the systems for the single-array track and the multiple-array
    track of the 5th CHiME Challenge. The final system is a combination of multiple
    systems, using Confusion Network Combination (CNC). The different systems presented
    here are utilizing different front-ends and training sets for a Bidirectional
    Long Short-Term Memory (BLSTM) Acoustic Model (AM). The front-end was replaced
    by enhancements provided by Paderborn University [1]. The back-end has been implemented
    using RASR [2] and RETURNN [3]. Additionally, a system combination including the
    hypothesis word graphs from the system of the submission [1] has been performed,
    which results in the final best system.
author:
- first_name: Markus
  full_name: Kitza, Markus
  last_name: Kitza
- first_name: Wilfried
  full_name: Michel, Wilfried
  last_name: Michel
- first_name: Christoph
  full_name: Boeddeker, Christoph
  id: '40767'
  last_name: Boeddeker
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Tobias
  full_name: Menne, Tobias
  last_name: Menne
- first_name: Ralf
  full_name: Schlüter, Ralf
  last_name: Schlüter
- first_name: Hermann
  full_name: Ney, Hermann
  last_name: Ney
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Lukas
  full_name: Drude, Lukas
  id: '11213'
  last_name: Drude
- first_name: Jahn
  full_name: Heymann, Jahn
  id: '9168'
  last_name: Heymann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Kitza M, Michel W, Boeddeker C, et al. The RWTH/UPB System Combination for
    the CHiME 2018 Workshop. In: <i>Proc. CHiME 2018 Workshop on Speech Processing
    in Everyday Environments, Hyderabad, India</i>. ; 2018.'
  apa: Kitza, M., Michel, W., Boeddeker, C., Heitkaemper, J., Menne, T., Schlüter,
    R., Ney, H., Schmalenstroeer, J., Drude, L., Heymann, J., &#38; Haeb-Umbach, R.
    (2018). The RWTH/UPB System Combination for the CHiME 2018 Workshop. <i>Proc.
    CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
    India</i>.
  bibtex: '@inproceedings{Kitza_Michel_Boeddeker_Heitkaemper_Menne_Schlüter_Ney_Schmalenstroeer_Drude_Heymann_et
    al._2018, title={The RWTH/UPB System Combination for the CHiME 2018 Workshop},
    booktitle={Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
    Hyderabad, India}, author={Kitza, Markus and Michel, Wilfried and Boeddeker, Christoph
    and Heitkaemper, Jens and Menne, Tobias and Schlüter, Ralf and Ney, Hermann and
    Schmalenstroeer, Joerg and Drude, Lukas and Heymann, Jahn and et al.}, year={2018}
    }'
  chicago: Kitza, Markus, Wilfried Michel, Christoph Boeddeker, Jens Heitkaemper,
    Tobias Menne, Ralf Schlüter, Hermann Ney, et al. “The RWTH/UPB System Combination
    for the CHiME 2018 Workshop.” In <i>Proc. CHiME 2018 Workshop on Speech Processing
    in Everyday Environments, Hyderabad, India</i>, 2018.
  ieee: M. Kitza <i>et al.</i>, “The RWTH/UPB System Combination for the CHiME 2018
    Workshop,” 2018.
  mla: Kitza, Markus, et al. “The RWTH/UPB System Combination for the CHiME 2018 Workshop.”
    <i>Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments, Hyderabad,
    India</i>, 2018.
  short: 'M. Kitza, W. Michel, C. Boeddeker, J. Heitkaemper, T. Menne, R. Schlüter,
    H. Ney, J. Schmalenstroeer, L. Drude, J. Heymann, R. Haeb-Umbach, in: Proc. CHiME
    2018 Workshop on Speech Processing in Everyday Environments, Hyderabad, India,
    2018.'
date_created: 2019-07-12T05:29:58Z
date_updated: 2023-10-26T08:12:14Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/INTERSPEECH_2018_Heitkaemper_RWTH_Paper.pdf
oa: '1'
publication: Proc. CHiME 2018 Workshop on Speech Processing in Everyday Environments,
  Hyderabad, India
quality_controlled: '1'
status: public
title: The RWTH/UPB System Combination for the CHiME 2018 Workshop
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11836'
abstract:
- lang: eng
  text: Due to their distributed nature wireless acoustic sensor networks offer great
    potential for improved signal acquisition, processing and classification for applications
    such as monitoring and surveillance, home automation, or hands-free telecommunication.
    To reduce the communication demand with a central server and to raise the privacy
    level it is desirable to perform processing at node level. The limited processing
    and memory capabilities on a sensor node, however, stand in contrast to the compute
    and memory intensive deep learning algorithms used in modern speech and audio
    processing. In this work, we perform benchmarking of commonly used convolutional
    and recurrent neural network architectures on a Raspberry Pi based acoustic sensor
    node. We show that it is possible to run medium-sized neural network topologies
    used for speech enhancement and speech recognition in real time. For acoustic
    event recognition, where predictions in a lower temporal resolution are sufficient,
    it is even possible to run current state-of-the-art deep convolutional models
    with a real-time-factor of 0:11.
author:
- first_name: Janek
  full_name: Ebbers, Janek
  id: '34851'
  last_name: Ebbers
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Ebbers J, Heitkaemper J, Schmalenstroeer J, Haeb-Umbach R. Benchmarking Neural
    Network Architectures for Acoustic Sensor Networks. In: <i>ITG 2018, Oldenburg,
    Germany</i>. ; 2018.'
  apa: Ebbers, J., Heitkaemper, J., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2018).
    Benchmarking Neural Network Architectures for Acoustic Sensor Networks. <i>ITG
    2018, Oldenburg, Germany</i>.
  bibtex: '@inproceedings{Ebbers_Heitkaemper_Schmalenstroeer_Haeb-Umbach_2018, title={Benchmarking
    Neural Network Architectures for Acoustic Sensor Networks}, booktitle={ITG 2018,
    Oldenburg, Germany}, author={Ebbers, Janek and Heitkaemper, Jens and Schmalenstroeer,
    Joerg and Haeb-Umbach, Reinhold}, year={2018} }'
  chicago: Ebbers, Janek, Jens Heitkaemper, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach.
    “Benchmarking Neural Network Architectures for Acoustic Sensor Networks.” In <i>ITG
    2018, Oldenburg, Germany</i>, 2018.
  ieee: J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, and R. Haeb-Umbach, “Benchmarking
    Neural Network Architectures for Acoustic Sensor Networks,” 2018.
  mla: Ebbers, Janek, et al. “Benchmarking Neural Network Architectures for Acoustic
    Sensor Networks.” <i>ITG 2018, Oldenburg, Germany</i>, 2018.
  short: 'J. Ebbers, J. Heitkaemper, J. Schmalenstroeer, R. Haeb-Umbach, in: ITG 2018,
    Oldenburg, Germany, 2018.'
date_created: 2019-07-12T05:29:11Z
date_updated: 2023-10-26T08:12:40Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Paper.pdf
oa: '1'
publication: ITG 2018, Oldenburg, Germany
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2018/ITG_2018_Ebbers_Poster.pdf
status: public
title: Benchmarking Neural Network Architectures for Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2018'
...
---
_id: '11743'
abstract:
- lang: eng
  text: This contribution introduces a novel causal a priori signal-to-noise ratio
    (SNR) estimator for single-channel speech enhancement. To exploit the advantages
    of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM)
    domain is introduced where an a priori SNR estimation is carried out. In this
    domain, the NAOM coefficients of noise and clean speech signals are modeled by
    a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the
    parameters of the noise model are calculated from the noise power spectral density
    estimates, the speechWMM parameters are estimated from the noisy signal by applying
    a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate
    of the a priori SNR is developed. The experiments in different noisy environments
    show the superiority of the proposed estimator compared to the well-known decision-directed
    approach in terms of estimation error, estimator variance and speech quality of
    the enhanced signals when used for speech enhancement.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Jens
  full_name: Heitkaemper, Jens
  id: '27643'
  last_name: Heitkaemper
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Heitkaemper J, Haeb-Umbach R. A Priori SNR Estimation Using Weibull
    Mixture Model. In: <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>. ;
    2016.'
  apa: Chinaev, A., Heitkaemper, J., &#38; Haeb-Umbach, R. (2016). A Priori SNR Estimation
    Using Weibull Mixture Model. In <i>12. ITG Fachtagung Sprachkommunikation (ITG
    2016)</i>.
  bibtex: '@inproceedings{Chinaev_Heitkaemper_Haeb-Umbach_2016, title={A Priori SNR
    Estimation Using Weibull Mixture Model}, booktitle={12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)}, author={Chinaev, Aleksej and Heitkaemper, Jens and Haeb-Umbach, Reinhold},
    year={2016} }'
  chicago: Chinaev, Aleksej, Jens Heitkaemper, and Reinhold Haeb-Umbach. “A Priori
    SNR Estimation Using Weibull Mixture Model.” In <i>12. ITG Fachtagung Sprachkommunikation
    (ITG 2016)</i>, 2016.
  ieee: A. Chinaev, J. Heitkaemper, and R. Haeb-Umbach, “A Priori SNR Estimation Using
    Weibull Mixture Model,” in <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>,
    2016.
  mla: Chinaev, Aleksej, et al. “A Priori SNR Estimation Using Weibull Mixture Model.”
    <i>12. ITG Fachtagung Sprachkommunikation (ITG 2016)</i>, 2016.
  short: 'A. Chinaev, J. Heitkaemper, R. Haeb-Umbach, in: 12. ITG Fachtagung Sprachkommunikation
    (ITG 2016), 2016.'
date_created: 2019-07-12T05:27:24Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16.pdf
oa: '1'
publication: 12. ITG Fachtagung Sprachkommunikation (ITG 2016)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2016/ChHeiHa16_Presentation.pdf
status: public
title: A Priori SNR Estimation Using Weibull Mixture Model
type: conference
user_id: '44006'
year: '2016'
...