---
_id: '49109'
abstract:
- lang: eng
  text: "We propose a diarization system, that estimates “who spoke when” based on
    spatial information, to be used as a front-end of a meeting transcription system
    running on the signals gathered from an acoustic sensor network (ASN). Although
    the\r\nspatial distribution of the microphones is advantageous, exploiting the
    spatial diversity for diarization and signal enhancement is challenging, because
    the microphones’ positions are typically unknown, and the recorded signals are
    initially unsynchronized in general. Here, we approach these issues by first blindly
    synchronizing the signals and then estimating time differences of arrival (TDOAs).
    The TDOA information is exploited to estimate the speakers’ activity, even in
    the presence of multiple speakers being simultaneously active. This speaker activity
    information serves as a guide for a spatial mixture model, on which basis the
    individual speaker’s signals are extracted via beamforming. Finally, the extracted
    signals are forwarded to a speech recognizer. Additionally, a novel initialization
    scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments
    conducted on real recordings from the LibriWASN data set have shown that our proposed
    system is advantageous compared to a system using a spatial mixture model, which
    does not make use\r\nof external diarization information."
author:
- first_name: Tobias
  full_name: Gburrek, Tobias
  id: '44006'
  last_name: Gburrek
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Spatial Diarization for Meeting
    Transcription with Ad-Hoc Acoustic Sensor Networks. In: <i>Proc. Asilomar Conference
    on Signals, Systems, and Computers</i>. ; 2023.'
  apa: Gburrek, T., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2023). Spatial Diarization
    for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. <i>Proc. Asilomar
    Conference on Signals, Systems, and Computers</i>. 57th Asilomar Conference on
    Signals, Systems, and Computers.
  bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2023, title={Spatial
    Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}, booktitle={Proc.
    Asilomar Conference on Signals, Systems, and Computers}, author={Gburrek, Tobias
    and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2023} }'
  chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Spatial
    Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” In
    <i>Proc. Asilomar Conference on Signals, Systems, and Computers</i>, 2023.
  ieee: T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Spatial Diarization for
    Meeting Transcription with Ad-Hoc Acoustic Sensor Networks,” presented at the
    57th Asilomar Conference on Signals, Systems, and Computers, 2023.
  mla: Gburrek, Tobias, et al. “Spatial Diarization for Meeting Transcription with
    Ad-Hoc Acoustic Sensor Networks.” <i>Proc. Asilomar Conference on Signals, Systems,
    and Computers</i>, 2023.
  short: 'T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: Proc. Asilomar Conference
    on Signals, Systems, and Computers, 2023.'
conference:
  end_date: 2023-11-01
  name: 57th Asilomar Conference on Signals, Systems, and Computers
  start_date: 2023-10-31
date_created: 2023-11-22T07:52:29Z
date_updated: 2023-11-22T07:58:49Z
ddc:
- '004'
department:
- _id: '54'
file:
- access_level: open_access
  content_type: application/pdf
  creator: schmalen
  date_created: 2023-11-22T07:51:18Z
  date_updated: 2023-11-22T07:58:49Z
  file_id: '49110'
  file_name: asilomar.pdf
  file_size: 212317
  relation: main_file
file_date_updated: 2023-11-22T07:58:49Z
has_accepted_license: '1'
keyword:
- Diarization
- time difference of arrival
- ad-hoc acoustic sensor network
- meeting transcription
language:
- iso: eng
oa: '1'
publication: Proc. Asilomar Conference on Signals, Systems, and Computers
quality_controlled: '1'
status: public
title: Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2023'
...
---
_id: '25281'
abstract:
- lang: eng
  text: "Wireless Acoustic Sensor Networks (WASNs) have a wide range of audio signal
    processing applications. Due to the spatial diversity of the microphone and their
    relative position to the acoustic source, not all microphones are equally useful
    for subsequent audio signal processing tasks, nor do they all have the same wireless
    data transmission rates. Hence, a central task in WASNs is to balance a microphone’s
    estimated acoustic utility against its transmission delay, selecting a best-possible
    subset of microphones to record audio signals.\r\n\r\nIn this work, we use reinforcement
    learning to decide if a microphone should be used or switched off to maximize
    the acoustic quality at low transmission delays, while minimizing switching frequency.
    In experiments with moving sources in a simulated acoustic environment, our method
    outperforms naive baseline comparisons"
author:
- first_name: Haitham
  full_name: Afifi, Haitham
  id: '65718'
  last_name: Afifi
- first_name: Michael
  full_name: Guenther, Michael
  last_name: Guenther
- first_name: Andreas
  full_name: Brendel, Andreas
  last_name: Brendel
- first_name: Holger
  full_name: Karl, Holger
  id: '126'
  last_name: Karl
- first_name: Walter
  full_name: Kellermann, Walter
  last_name: Kellermann
citation:
  ama: 'Afifi H, Guenther M, Brendel A, Karl H, Kellermann W. Reinforcement Learning-based
    Microphone Selection in Wireless Acoustic Sensor Networks considering Network
    and Acoustic Utilities. In: <i>14. ITG Conference on Speech Communication (ITG
    2021)</i>. ; 2021.'
  apa: Afifi, H., Guenther, M., Brendel, A., Karl, H., &#38; Kellermann, W. (2021).
    Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
    Networks considering Network and Acoustic Utilities. <i>14. ITG Conference on
    Speech Communication (ITG 2021)</i>.
  bibtex: '@inproceedings{Afifi_Guenther_Brendel_Karl_Kellermann_2021, title={Reinforcement
    Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
    Network and Acoustic Utilities}, booktitle={14. ITG Conference on Speech Communication
    (ITG 2021)}, author={Afifi, Haitham and Guenther, Michael and Brendel, Andreas
    and Karl, Holger and Kellermann, Walter}, year={2021} }'
  chicago: Afifi, Haitham, Michael Guenther, Andreas Brendel, Holger Karl, and Walter
    Kellermann. “Reinforcement Learning-Based Microphone Selection in Wireless Acoustic
    Sensor Networks Considering Network and Acoustic Utilities.” In <i>14. ITG Conference
    on Speech Communication (ITG 2021)</i>, 2021.
  ieee: H. Afifi, M. Guenther, A. Brendel, H. Karl, and W. Kellermann, “Reinforcement
    Learning-based Microphone Selection in Wireless Acoustic Sensor Networks considering
    Network and Acoustic Utilities,” 2021.
  mla: Afifi, Haitham, et al. “Reinforcement Learning-Based Microphone Selection in
    Wireless Acoustic Sensor Networks Considering Network and Acoustic Utilities.”
    <i>14. ITG Conference on Speech Communication (ITG 2021)</i>, 2021.
  short: 'H. Afifi, M. Guenther, A. Brendel, H. Karl, W. Kellermann, in: 14. ITG Conference
    on Speech Communication (ITG 2021), 2021.'
date_created: 2021-10-04T10:59:50Z
date_updated: 2022-01-06T06:56:59Z
ddc:
- '620'
file:
- access_level: closed
  content_type: application/pdf
  creator: hafifi
  date_created: 2021-10-04T10:58:07Z
  date_updated: 2021-10-04T10:58:07Z
  file_id: '25282'
  file_name: ITG_2021_paper_26 (3).pdf
  file_size: 283616
  relation: main_file
  success: 1
file_date_updated: 2021-10-04T10:58:07Z
has_accepted_license: '1'
keyword:
- microphone utility
- microphone selection
- wireless acoustic sensor network
- network delay
- reinforcement learning
language:
- iso: eng
project:
- _id: '27'
  name: Akustische Sensornetzwerke - Teilprojekt "Verteilte akustische Signalverarbeitung
    über funkbasierte Sensornetzwerke
publication: 14. ITG Conference on Speech Communication (ITG 2021)
status: public
title: Reinforcement Learning-based Microphone Selection in Wireless Acoustic Sensor
  Networks considering Network and Acoustic Utilities
type: conference
user_id: '65718'
year: '2021'
...
---
_id: '11891'
abstract:
- lang: eng
  text: In this paper we present a combined hardware/software approach for synchronizing
    the sampling clocks of an acoustic sensor network. A first clock frequency offset
    estimate is obtained by a time stamp exchange protocol with a low data rate and
    computational requirements. The estimate is then postprocessed by a Kalman filter
    which exploits the specific properties of the statistics of the frequency offset
    estimation error. In long term experiments the deviation between the sampling
    oscillators of two sensor nodes never exceeded half a sample with a wired and
    with a wireless link between the nodes. The achieved precision enables the estimation
    of time difference of arrival values across different hardware devices without
    sharing a common sampling hardware.
author:
- 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: 'Schmalenstroeer J, Haeb-Umbach R. Sampling Rate Synchronisation in Acoustic
    Sensor Networks with a Pre-Trained Clock Skew Error Model. In: <i>21th European
    Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013.'
  apa: Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). Sampling Rate Synchronisation
    in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model. <i>21th
    European Signal Processing Conference (EUSIPCO 2013)</i>.
  bibtex: '@inproceedings{Schmalenstroeer_Haeb-Umbach_2013, title={Sampling Rate Synchronisation
    in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model}, booktitle={21th
    European Signal Processing Conference (EUSIPCO 2013)}, author={Schmalenstroeer,
    Joerg and Haeb-Umbach, Reinhold}, year={2013} }'
  chicago: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation
    in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” In <i>21th
    European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.
  ieee: J. Schmalenstroeer and R. Haeb-Umbach, “Sampling Rate Synchronisation in Acoustic
    Sensor Networks with a Pre-Trained Clock Skew Error Model,” 2013.
  mla: Schmalenstroeer, Joerg, and Reinhold Haeb-Umbach. “Sampling Rate Synchronisation
    in Acoustic Sensor Networks with a Pre-Trained Clock Skew Error Model.” <i>21th
    European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.
  short: 'J. Schmalenstroeer, R. Haeb-Umbach, in: 21th European Signal Processing
    Conference (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:30:15Z
date_updated: 2023-10-26T08:11:01Z
department:
- _id: '54'
keyword:
- synchronization
- acoustic sensor network
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
quality_controlled: '1'
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/SchHaeb2013_Presentation.pdf
status: public
title: Sampling Rate Synchronisation in Acoustic Sensor Networks with a Pre-Trained
  Clock Skew Error Model
type: conference
user_id: '460'
year: '2013'
...