---
_id: '11716'
abstract:
- lang: eng
  text: The accuracy of automatic speech recognition systems in noisy and reverberant
    environments can be improved notably by exploiting the uncertainty of the estimated
    speech features using so-called uncertainty-of-observation techniques. In this
    paper, we introduce a new Bayesian decision rule that can serve as a mathematical
    framework from which both known and new uncertainty-of-observation techniques
    can be either derived or approximated. The new decision rule in its direct form
    leads to the new significance decoding approach for Gaussian mixture models, which
    results in better performance compared to standard uncertainty-of-observation
    techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
  full_name: Abdelaziz, Ahmed H.
  last_name: Abdelaziz
- first_name: Steffen
  full_name: Zeiler, Steffen
  last_name: Zeiler
- first_name: Dorothea
  full_name: Kolossa, Dorothea
  last_name: Kolossa
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
    decoding. In: <i>Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
    Conference On</i>. ; 2013:6827-6831. doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>'
  apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., &#38; Haeb-Umbach,
    R. (2013). GMM-based significance decoding. In <i>Acoustics, Speech and Signal
    Processing (ICASSP), 2013 IEEE International Conference on</i> (pp. 6827–6831).
    <a href="https://doi.org/10.1109/ICASSP.2013.6638984">https://doi.org/10.1109/ICASSP.2013.6638984</a>
  bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
    significance decoding}, DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>},
    booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
    Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
    and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
    }'
  chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
    and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In <i>Acoustics,
    Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>,
    6827–31, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638984">https://doi.org/10.1109/ICASSP.2013.6638984</a>.
  ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
    significance decoding,” in <i>Acoustics, Speech and Signal Processing (ICASSP),
    2013 IEEE International Conference on</i>, 2013, pp. 6827–6831.
  mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” <i>Acoustics,
    Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On</i>,
    2013, pp. 6827–31, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638984">10.1109/ICASSP.2013.6638984</a>.
  short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
    On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
  Conference on
publication_identifier:
  issn:
  - 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11740'
abstract:
- lang: eng
  text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
    the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
    We assume the distortion to be white Gaussian noise of known mean and variance.
    An approximate conjugate prior of the GMM parameters is derived allowing for a
    computationally efficient implementation in a sequential estimation framework.
    Simulations on artificially generated data demonstrate the superiority of the
    proposed method compared to the Maximum Likelihood technique and to the ordinary
    MAP approach, whose estimates are corrected by the known statistics of the distortion
    in a straightforward manner.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
    Mixture Model in the Presence of Noisy Observations. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:3352-3356.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations. In <i>38th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>
    (pp. 3352–3356). <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
    the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
    pages={3352–3356} }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
    Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i>, 3352–56, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638279">https://doi.org/10.1109/ICASSP.2013.6638279</a>.
  ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
    a Gaussian Mixture Model in the Presence of Noisy Observations,” in <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–3356.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
    of a Gaussian Mixture Model in the Presence of Noisy Observations.” <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 3352–56, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638279">10.1109/ICASSP.2013.6638279</a>.
  short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
  of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11742'
abstract:
- lang: eng
  text: In this paper we present an improved version of the recently proposed Maximum
    A-Posteriori (MAP) based noise power spectral density estimator. An empirical
    bias compensation and bandwidth adjustment reduce bias and variance of the noise
    variance estimates. The main advantage of the MAP-based postprocessor is its low
    estimation variance. The estimator is employed in the second stage of a two-stage
    single-channel speech enhancement system, where eight different state-of-the-art
    noise tracking algorithms were tested in the first stage. While the postprocessor
    hardly affects the results in stationary noise scenarios, it becomes the more
    effective the more nonstationary the noise is. The proposed postprocessor was
    able to improve all systems in babble noise w.r.t. the perceptual evaluation of
    speech quality performance.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Jalal
  full_name: Taghia, Jalal
  last_name: Taghia
- first_name: Rainer
  full_name: Martin, Rainer
  last_name: Martin
citation:
  ama: 'Chinaev A, Haeb-Umbach R, Taghia J, Martin R. Improved Single-Channel Nonstationary
    Noise Tracking by an Optimized MAP-based Postprocessor. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:7477-7481.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6639116">10.1109/ICASSP.2013.6639116</a>'
  apa: Chinaev, A., Haeb-Umbach, R., Taghia, J., &#38; Martin, R. (2013). Improved
    Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor.
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i> (pp. 7477–7481). <a href="https://doi.org/10.1109/ICASSP.2013.6639116">https://doi.org/10.1109/ICASSP.2013.6639116</a>
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_Taghia_Martin_2013, title={Improved
    Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6639116">10.1109/ICASSP.2013.6639116</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold and Taghia,
    Jalal and Martin, Rainer}, year={2013}, pages={7477–7481} }'
  chicago: Chinaev, Aleksej, Reinhold Haeb-Umbach, Jalal Taghia, and Rainer Martin.
    “Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-Based
    Postprocessor.” In <i>38th International Conference on Acoustics, Speech and Signal
    Processing (ICASSP 2013)</i>, 7477–81, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6639116">https://doi.org/10.1109/ICASSP.2013.6639116</a>.
  ieee: A. Chinaev, R. Haeb-Umbach, J. Taghia, and R. Martin, “Improved Single-Channel
    Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor,” in <i>38th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 7477–7481.
  mla: Chinaev, Aleksej, et al. “Improved Single-Channel Nonstationary Noise Tracking
    by an Optimized MAP-Based Postprocessor.” <i>38th International Conference on
    Acoustics, Speech and Signal Processing (ICASSP 2013)</i>, 2013, pp. 7477–81,
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6639116">10.1109/ICASSP.2013.6639116</a>.
  short: 'A. Chinaev, R. Haeb-Umbach, J. Taghia, R. Martin, in: 38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013, pp.
    7477–7481.'
date_created: 2019-07-12T05:27:23Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6639116
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13.pdf
oa: '1'
page: 7477-7481
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHaTaRa13_Poster.pdf
status: public
title: Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based
  Postprocessor
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11816'
abstract:
- lang: eng
  text: In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters
    of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the
    resulting Expectation Maximization (EM) algorithm delivers virtually biasfree
    and efficient estimates, and we discuss its convergence properties. We also discuss
    optimal classification in the presence of censored data. Censored data are frequently
    encountered in wireless LAN positioning systems based on the fingerprinting method
    employing signal strength measurements, due to the limited sensitivity of the
    portable devices. Experiments both on simulated and real-world data demonstrate
    the effectiveness of the proposed algorithms.
author:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Hoang MK, Haeb-Umbach R. Parameter estimation and classification of censored
    Gaussian data with application to WiFi indoor positioning. In: <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:3721-3725.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>'
  apa: Hoang, M. K., &#38; Haeb-Umbach, R. (2013). Parameter estimation and classification
    of censored Gaussian data with application to WiFi indoor positioning. In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>
    (pp. 3721–3725). <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>
  bibtex: '@inproceedings{Hoang_Haeb-Umbach_2013, title={Parameter estimation and
    classification of censored Gaussian data with application to WiFi indoor positioning},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>},
    booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2013)}, author={Hoang, Manh Kha and Haeb-Umbach, Reinhold}, year={2013},
    pages={3721–3725} }'
  chicago: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    3721–25, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6638353">https://doi.org/10.1109/ICASSP.2013.6638353</a>.
  ieee: M. K. Hoang and R. Haeb-Umbach, “Parameter estimation and classification of
    censored Gaussian data with application to WiFi indoor positioning,” in <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–3725.
  mla: Hoang, Manh Kha, and Reinhold Haeb-Umbach. “Parameter Estimation and Classification
    of Censored Gaussian Data with Application to WiFi Indoor Positioning.” <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    2013, pp. 3721–25, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6638353">10.1109/ICASSP.2013.6638353</a>.
  short: 'M.K. Hoang, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech, and Signal Processing (ICASSP 2013), 2013, pp. 3721–3725.'
date_created: 2019-07-12T05:28:48Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638353
keyword:
- Gaussian processes
- Global Positioning System
- convergence
- expectation-maximisation algorithm
- fingerprint identification
- indoor radio
- signal classification
- wireless LAN
- EM algorithm
- ML estimation
- WiFi indoor positioning
- censored Gaussian data classification
- clipped data
- convergence properties
- expectation maximization algorithm
- fingerprinting method
- maximum likelihood estimation
- optimal classification
- parameters estimation
- portable devices sensitivity
- signal strength measurements
- wireless LAN positioning systems
- Convergence
- IEEE 802.11 Standards
- Maximum likelihood estimation
- Parameter estimation
- Position measurement
- Training
- Indoor positioning
- censored data
- expectation maximization
- signal strength
- wireless LAN
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013.pdf
oa: '1'
page: 3721-3725
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/HoHa2013_Poster.pdf
status: public
title: Parameter estimation and classification of censored Gaussian data with application
  to WiFi indoor positioning
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11917'
abstract:
- lang: eng
  text: In this paper we present a speech presence probability (SPP) estimation algorithmwhich
    exploits both temporal and spectral correlations of speech. To this end, the SPP
    estimation is formulated as the posterior probability estimation of the states
    of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm
    to decode the 2D-HMM which is based on the turbo principle. The experimental results
    show that indeed the SPP estimates improve from iteration to iteration, and further
    clearly outperform another state-of-the-art SPP estimation algorithm.
author:
- first_name: Dang Hai Tran
  full_name: Vu, Dang Hai Tran
  last_name: Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Vu DHT, Haeb-Umbach R. Using the turbo principle for exploiting temporal and
    spectral correlations in speech presence probability estimation. In: <i>38th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)</i>. ; 2013:863-867.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>'
  apa: Vu, D. H. T., &#38; Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
    temporal and spectral correlations in speech presence probability estimation.
    In <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i> (pp. 863–867). <a href="https://doi.org/10.1109/ICASSP.2013.6637771">https://doi.org/10.1109/ICASSP.2013.6637771</a>
  bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
    exploiting temporal and spectral correlations in speech presence probability estimation},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>},
    booktitle={38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)}, author={Vu, Dang Hai Tran and Haeb-Umbach, Reinhold}, year={2013},
    pages={863–867} }'
  chicago: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle
    for Exploiting Temporal and Spectral Correlations in Speech Presence Probability
    Estimation.” In <i>38th International Conference on Acoustics, Speech and Signal
    Processing (ICASSP 2013)</i>, 863–67, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6637771">https://doi.org/10.1109/ICASSP.2013.6637771</a>.
  ieee: D. H. T. Vu and R. Haeb-Umbach, “Using the turbo principle for exploiting
    temporal and spectral correlations in speech presence probability estimation,”
    in <i>38th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2013)</i>, 2013, pp. 863–867.
  mla: Vu, Dang Hai Tran, and Reinhold Haeb-Umbach. “Using the Turbo Principle for
    Exploiting Temporal and Spectral Correlations in Speech Presence Probability Estimation.”
    <i>38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
    2013)</i>, 2013, pp. 863–67, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637771">10.1109/ICASSP.2013.6637771</a>.
  short: 'D.H.T. Vu, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2013), 2013, pp. 863–867.'
date_created: 2019-07-12T05:30:45Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637771
keyword:
- correlation methods
- estimation theory
- hidden Markov models
- iterative methods
- probability
- spectral analysis
- speech processing
- 2D HMM
- SPP estimates
- iterative algorithm
- posterior probability estimation
- spectral correlation
- speech presence probability estimation
- state-of-the-art SPP estimation algorithm
- temporal correlation
- turbo principle
- two-dimensional hidden Markov model
- Correlation
- Decoding
- Estimation
- Iterative decoding
- Noise
- Speech
- Vectors
language:
- iso: eng
page: 863-867
publication: 38th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
status: public
title: Using the turbo principle for exploiting temporal and spectral correlations
  in speech presence probability estimation
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11832'
abstract:
- lang: eng
  text: In this paper we propose an approach to retrieve the absolute geometry of
    an acoustic sensor network, consisting of spatially distributed microphone arrays,
    from reverberant speech input. The calibration relies on direction of arrival
    measurements of the individual arrays. The proposed calibration algorithm is derived
    from a maximum-likelihood approach employing circular statistics. Since a sensor
    node consists of a microphone array with known intra-array geometry, we are able
    to obtain an absolute geometry estimate, including angles and distances. Simulation
    results demonstrate the effectiveness of the approach.
author:
- first_name: Florian
  full_name: Jacob, Florian
  last_name: Jacob
- 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: 'Jacob F, Schmalenstroeer J, Haeb-Umbach R. DoA-Based Microphone Array Position
    Self-Calibration Using Circular Statistic. In: <i>38th International Conference
    on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>. ; 2013:116-120.
    doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637620">10.1109/ICASSP.2013.6637620</a>'
  apa: Jacob, F., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). DoA-Based Microphone
    Array Position Self-Calibration Using Circular Statistic. <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 116–120.
    <a href="https://doi.org/10.1109/ICASSP.2013.6637620">https://doi.org/10.1109/ICASSP.2013.6637620</a>
  bibtex: '@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2013, title={DoA-Based
    Microphone Array Position Self-Calibration Using Circular Statistic}, DOI={<a
    href="https://doi.org/10.1109/ICASSP.2013.6637620">10.1109/ICASSP.2013.6637620</a>},
    booktitle={38th International Conference on Acoustics, Speech, and Signal Processing
    (ICASSP 2013)}, author={Jacob, Florian and Schmalenstroeer, Joerg and Haeb-Umbach,
    Reinhold}, year={2013}, pages={116–120} }'
  chicago: Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “DoA-Based
    Microphone Array Position Self-Calibration Using Circular Statistic.” In <i>38th
    International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>,
    116–20, 2013. <a href="https://doi.org/10.1109/ICASSP.2013.6637620">https://doi.org/10.1109/ICASSP.2013.6637620</a>.
  ieee: 'F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “DoA-Based Microphone Array
    Position Self-Calibration Using Circular Statistic,” in <i>38th International
    Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013)</i>, 2013,
    pp. 116–120, doi: <a href="https://doi.org/10.1109/ICASSP.2013.6637620">10.1109/ICASSP.2013.6637620</a>.'
  mla: Jacob, Florian, et al. “DoA-Based Microphone Array Position Self-Calibration
    Using Circular Statistic.” <i>38th International Conference on Acoustics, Speech,
    and Signal Processing (ICASSP 2013)</i>, 2013, pp. 116–20, doi:<a href="https://doi.org/10.1109/ICASSP.2013.6637620">10.1109/ICASSP.2013.6637620</a>.
  short: 'F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: 38th International Conference
    on Acoustics, Speech, and Signal Processing (ICASSP 2013), 2013, pp. 116–120.'
date_created: 2019-07-12T05:29:07Z
date_updated: 2023-10-26T08:11:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6637620
keyword:
- Geometry calibration
- microphone arrays
- position self-calibration
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/JacSchHae_ICASSP2013_Rev2.pdf
oa: '1'
page: 116-120
publication: 38th International Conference on Acoustics, Speech, and Signal Processing
  (ICASSP 2013)
publication_identifier:
  issn:
  - 1520-6149
quality_controlled: '1'
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/JaScHa13_Presentation.pdf
status: public
title: DoA-Based Microphone Array Position Self-Calibration Using Circular Statistic
type: conference
user_id: '460'
year: '2013'
...