---
_id: '22528'
abstract:
- lang: eng
  text: Due to the ad hoc nature of wireless acoustic sensor networks, the position
    of the sensor nodes is typically unknown. This contribution proposes a technique
    to estimate the position and orientation of the sensor nodes from the recorded
    speech signals. The method assumes that a node comprises a microphone array with
    synchronously sampled microphones rather than a single microphone, but does not
    require the sampling clocks of the nodes to be synchronized. From the observed
    audio signals, the distances between the acoustic sources and arrays, as well
    as the directions of arrival, are estimated. They serve as input to a non-linear
    least squares problem, from which both the sensor nodes’ positions and orientations,
    as well as the source positions, are alternatingly estimated in an iterative process.
    Given one set of unknowns, i.e., either the source positions or the sensor nodes’
    geometry, the other set of unknowns can be computed in closed-form. The proposed
    approach is computationally efficient and the first one, which employs both distance
    and directional information for geometry calibration in a common cost function.
    Since both distance and direction of arrival measurements suffer from outliers,
    e.g., caused by strong reflections of the sound waves on the surfaces of the room,
    we introduce measures to deemphasize or remove unreliable measurements. Additionally,
    we discuss modifications of our previously proposed deep neural network-based
    acoustic distance estimator, to account not only for omnidirectional sources but
    also for directional sources. Simulation results show good positioning accuracy
    and compare very favorably with alternative approaches from the literature.
author:
- first_name: Tobias
  full_name: Gburrek, Tobias
  id: '44006'
  last_name: Gburrek
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Geometry calibration in wireless
    acoustic sensor networks utilizing DoA and distance information. <i>EURASIP Journal
    on Audio, Speech, and Music Processing</i>. Published online 2021. doi:<a href="https://doi.org/10.1186/s13636-021-00210-x">10.1186/s13636-021-00210-x</a>
  apa: Gburrek, T., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2021). Geometry calibration
    in wireless acoustic sensor networks utilizing DoA and distance information. <i>EURASIP
    Journal on Audio, Speech, and Music Processing</i>. <a href="https://doi.org/10.1186/s13636-021-00210-x">https://doi.org/10.1186/s13636-021-00210-x</a>
  bibtex: '@article{Gburrek_Schmalenstroeer_Haeb-Umbach_2021, title={Geometry calibration
    in wireless acoustic sensor networks utilizing DoA and distance information},
    DOI={<a href="https://doi.org/10.1186/s13636-021-00210-x">10.1186/s13636-021-00210-x</a>},
    journal={EURASIP Journal on Audio, Speech, and Music Processing}, author={Gburrek,
    Tobias and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2021} }'
  chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Geometry
    Calibration in Wireless Acoustic Sensor Networks Utilizing DoA and Distance Information.”
    <i>EURASIP Journal on Audio, Speech, and Music Processing</i>, 2021. <a href="https://doi.org/10.1186/s13636-021-00210-x">https://doi.org/10.1186/s13636-021-00210-x</a>.
  ieee: 'T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Geometry calibration
    in wireless acoustic sensor networks utilizing DoA and distance information,”
    <i>EURASIP Journal on Audio, Speech, and Music Processing</i>, 2021, doi: <a href="https://doi.org/10.1186/s13636-021-00210-x">10.1186/s13636-021-00210-x</a>.'
  mla: Gburrek, Tobias, et al. “Geometry Calibration in Wireless Acoustic Sensor Networks
    Utilizing DoA and Distance Information.” <i>EURASIP Journal on Audio, Speech,
    and Music Processing</i>, 2021, doi:<a href="https://doi.org/10.1186/s13636-021-00210-x">10.1186/s13636-021-00210-x</a>.
  short: T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, EURASIP Journal on Audio,
    Speech, and Music Processing (2021).
date_created: 2021-07-05T05:30:15Z
date_updated: 2023-11-17T06:36:17Z
department:
- _id: '54'
doi: 10.1186/s13636-021-00210-x
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://asmp-eurasipjournals.springeropen.com/articles/10.1186/s13636-021-00210-x
oa: '1'
publication: EURASIP Journal on Audio, Speech, and Music Processing
publication_identifier:
  issn:
  - 1687-4722
publication_status: published
quality_controlled: '1'
status: public
title: Geometry calibration in wireless acoustic sensor networks utilizing DoA and
  distance information
type: journal_article
user_id: '44006'
year: '2021'
...