---
_id: '11841'
abstract:
- lang: eng
  text: Recently, substantial progress has been made in the field of reverberant speech
    signal processing, including both single- and multichannel de-reverberation techniques,
    and automatic speech recognition (ASR) techniques robust to reverberation. To
    evaluate state-of-the-art algorithms and obtain new insights regarding potential
    future research directions, we propose a common evaluation framework including
    datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques.
    The proposed framework will be used as a common basis for the REVERB (REverberant
    Voice Enhancement and Recognition Benchmark) challenge. This paper describes the
    rationale behind the challenge, and provides a detailed description of the evaluation
    framework and benchmark results.
author:
- first_name: Keisuke
  full_name: Kinoshita, Keisuke
  last_name: Kinoshita
- first_name: Marc
  full_name: Delcroix, Marc
  last_name: Delcroix
- first_name: Takuya
  full_name: Yoshioka, Takuya
  last_name: Yoshioka
- first_name: Tomohiro
  full_name: Nakatani, Tomohiro
  last_name: Nakatani
- first_name: Emanuel
  full_name: Habets, Emanuel
  last_name: Habets
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Armin
  full_name: Sehr, Armin
  last_name: Sehr
- first_name: Walter
  full_name: Kellermann, Walter
  last_name: Kellermann
- first_name: Roland
  full_name: Maas, Roland
  last_name: Maas
- first_name: Sharon
  full_name: Gannot, Sharon
  last_name: Gannot
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
citation:
  ama: 'Kinoshita K, Delcroix M, Yoshioka T, et al. The reverb challenge: a common
    evaluation framework for dereverberation and recognition of reverberant speech.
    In: <i> IEEE Workshop on Applications of Signal Processing to Audio and Acoustics
    </i>. ; 2013:22-23.'
  apa: 'Kinoshita, K., Delcroix, M., Yoshioka, T., Nakatani, T., Habets, E., Haeb-Umbach,
    R., … Raj, B. (2013). The reverb challenge: a common evaluation framework for
    dereverberation and recognition of reverberant speech. In <i> IEEE Workshop on
    Applications of Signal Processing to Audio and Acoustics </i> (pp. 22–23).'
  bibtex: '@inproceedings{Kinoshita_Delcroix_Yoshioka_Nakatani_Habets_Haeb-Umbach_Leutnant_Sehr_Kellermann_Maas_et
    al._2013, title={The reverb challenge: a common evaluation framework for dereverberation
    and recognition of reverberant speech}, booktitle={ IEEE Workshop on Applications
    of Signal Processing to Audio and Acoustics }, author={Kinoshita, Keisuke and
    Delcroix, Marc and Yoshioka, Takuya and Nakatani, Tomohiro and Habets, Emanuel
    and Haeb-Umbach, Reinhold and Leutnant, Volker and Sehr, Armin and Kellermann,
    Walter and Maas, Roland and et al.}, year={2013}, pages={22–23} }'
  chicago: 'Kinoshita, Keisuke, Marc Delcroix, Takuya Yoshioka, Tomohiro Nakatani,
    Emanuel Habets, Reinhold Haeb-Umbach, Volker Leutnant, et al. “The Reverb Challenge:
    A Common Evaluation Framework for Dereverberation and Recognition of Reverberant
    Speech.” In <i> IEEE Workshop on Applications of Signal Processing to Audio and
    Acoustics </i>, 22–23, 2013.'
  ieee: 'K. Kinoshita <i>et al.</i>, “The reverb challenge: a common evaluation framework
    for dereverberation and recognition of reverberant speech,” in <i> IEEE Workshop
    on Applications of Signal Processing to Audio and Acoustics </i>, 2013, pp. 22–23.'
  mla: 'Kinoshita, Keisuke, et al. “The Reverb Challenge: A Common Evaluation Framework
    for Dereverberation and Recognition of Reverberant Speech.” <i> IEEE Workshop
    on Applications of Signal Processing to Audio and Acoustics </i>, 2013, pp. 22–23.'
  short: 'K. Kinoshita, M. Delcroix, T. Yoshioka, T. Nakatani, E. Habets, R. Haeb-Umbach,
    V. Leutnant, A. Sehr, W. Kellermann, R. Maas, S. Gannot, B. Raj, in:  IEEE Workshop
    on Applications of Signal Processing to Audio and Acoustics , 2013, pp. 22–23.'
date_created: 2019-07-12T05:29:17Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Reverberant speech
- dereverberation
- ASR
- evaluation
- challenge
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/Reverb2013.pdf
oa: '1'
page: ' 22-23 '
publication: ' IEEE Workshop on Applications of Signal Processing to Audio and Acoustics '
status: public
title: 'The reverb challenge: a common evaluation framework for dereverberation and
  recognition of reverberant speech'
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11862'
abstract:
- lang: eng
  text: In this contribution we extend a previously proposed Bayesian approach for
    the enhancement of reverberant logarithmic mel power spectral coefficients for
    robust automatic speech recognition to the additional compensation of background
    noise. A recently proposed observation model is employed whose time-variant observation
    error statistics are obtained as a side product of the inference of the a posteriori
    probability density function of the clean speech feature vectors. Further a reduction
    of the computational effort and the memory requirements are achieved by using
    a recursive formulation of the observation model. The performance of the proposed
    algorithms is first experimentally studied on a connected digits recognition task
    with artificially created noisy reverberant data. It is shown that the use of
    the time-variant observation error model leads to a significant error rate reduction
    at low signal-to-noise ratios compared to a time-invariant model. Further experiments
    were conducted on a 5000 word task recorded in a reverberant and noisy environment.
    A significant word error rate reduction was obtained demonstrating the effectiveness
    of the approach on real-world data.
author:
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Leutnant V, Krueger A, Haeb-Umbach R. Bayesian Feature Enhancement for Reverberation
    and Noise Robust Speech Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>. 2013;21(8):1640-1652. doi:<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition. <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, <i>21</i>(8), 1640–1652. <a href="https://doi.org/10.1109/TASL.2013.2258013">https://doi.org/10.1109/TASL.2013.2258013</a>
  bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>},
    number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2013},
    pages={1640–1652} }'
  chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Bayesian
    Feature Enhancement for Reverberation and Noise Robust Speech Recognition.” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i> 21, no. 8 (2013): 1640–52.
    <a href="https://doi.org/10.1109/TASL.2013.2258013">https://doi.org/10.1109/TASL.2013.2258013</a>.'
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement
    for Reverberation and Noise Robust Speech Recognition,” <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, vol. 21, no. 8, pp. 1640–1652, 2013.
  mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
    Noise Robust Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and Language
    Processing</i>, vol. 21, no. 8, 2013, pp. 1640–52, doi:<a href="https://doi.org/10.1109/TASL.2013.2258013">10.1109/TASL.2013.2258013</a>.
  short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
    and Language Processing 21 (2013) 1640–1652.
date_created: 2019-07-12T05:29:42Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2013.2258013
intvolume: '        21'
issue: '8'
keyword:
- Bayes methods
- compensation
- error statistics
- reverberation
- speech recognition
- Bayesian feature enhancement
- background noise
- clean speech feature vectors
- compensation
- connected digits recognition task
- error statistics
- memory requirements
- noisy reverberant data
- posteriori probability density function
- recursive formulation
- reverberant logarithmic mel power spectral coefficients
- robust automatic speech recognition
- signal-to-noise ratios
- time-variant observation
- word error rate reduction
- Robust automatic speech recognition
- model-based Bayesian feature enhancement
- observation model for reverberant and noisy speech
- recursive observation model
language:
- iso: eng
page: 1640-1652
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 21
year: '2013'
...
---
_id: '11909'
abstract:
- lang: eng
  text: 'We present a novel method to exploit correlations of adjacent time-frequency
    (TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually,
    these correlations are exploited by some heuristic smoothing techniques in the
    post-processing of the estimated soft TF masks. We propose a different approach:
    Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs)
    along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit
    both temporal and spectral correlations in the speech signal. Based on the principles
    of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward
    algorithm which operates alternatingly along the time and the frequency axis.
    Extrinsic information is exchanged between these steps such that increasingly
    better soft time-frequency masks are obtained, leading to improved speech separation
    performance in highly reverberant recording conditions.'
author:
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Tran Vu DH, Haeb-Umbach R. Blind Speech Separation Exploiting Temporal and
    Spectral Correlations Using Turbo Decoding of 2D-HMMs. In: <i>21th European Signal
    Processing Conference (EUSIPCO 2013)</i>. ; 2013.'
  apa: Tran Vu, D. H., &#38; Haeb-Umbach, R. (2013). Blind Speech Separation Exploiting
    Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs. In <i>21th
    European Signal Processing Conference (EUSIPCO 2013)</i>.
  bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2013, title={Blind Speech Separation
    Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs},
    booktitle={21th European Signal Processing Conference (EUSIPCO 2013)}, author={Tran
    Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }'
  chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting
    Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” In <i>21th
    European Signal Processing Conference (EUSIPCO 2013)</i>, 2013.
  ieee: D. H. Tran Vu and R. Haeb-Umbach, “Blind Speech Separation Exploiting Temporal
    and Spectral Correlations Using Turbo Decoding of 2D-HMMs,” in <i>21th European
    Signal Processing Conference (EUSIPCO 2013)</i>, 2013.
  mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Exploiting
    Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs.” <i>21th European
    Signal Processing Conference (EUSIPCO 2013)</i>, 2013.
  short: 'D.H. Tran Vu, R. Haeb-Umbach, in: 21th European Signal Processing Conference
    (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:30:36Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/TrHa2013_01.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/TrHa2013_01_Presentation.pdf
status: public
title: Blind Speech Separation Exploiting Temporal and Spectral Correlations Using
  Turbo Decoding of 2D-HMMs
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: '11921'
abstract:
- lang: eng
  text: In this paper we consider the unsupervised word discovery from phonetic input.
    We employ a word segmentation algorithm which simultaneously develops a lexicon,
    i.e., the transcription of a word in terms of a phone sequence, learns a n-gram
    language model describing word and word sequence probabilities, and carries out
    the segmentation itself. The underlying statistical model is that of a Pitman-Yor
    process, a concept known from Bayesian non-parametrics, which allows for an a
    priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor
    processes, language models of different order can be employed and nesting it with
    another hierarchy of Pitman-Yor processes on the phone level allows for backing
    off unknown word unigrams by phone m-grams. We present results on a large-vocabulary
    task, assuming an error-free phone sequence is given. We finish by discussing
    options how to cope with noisy phone sequences.
author:
- first_name: Oliver
  full_name: Walter, Oliver
  last_name: Walter
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Sourish
  full_name: Chaudhuri, Sourish
  last_name: Chaudhuri
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
citation:
  ama: 'Walter O, Haeb-Umbach R, Chaudhuri S, Raj B. Unsupervised Word Discovery from
    Phonetic Input Using Nested Pitman-Yor Language Modeling. In: <i>IEEE International
    Conference on Robotics and Automation (ICRA 2013)</i>. ; 2013.'
  apa: Walter, O., Haeb-Umbach, R., Chaudhuri, S., &#38; Raj, B. (2013). Unsupervised
    Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling.
    In <i>IEEE International Conference on Robotics and Automation (ICRA 2013)</i>.
  bibtex: '@inproceedings{Walter_Haeb-Umbach_Chaudhuri_Raj_2013, title={Unsupervised
    Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling},
    booktitle={IEEE International Conference on Robotics and Automation (ICRA 2013)},
    author={Walter, Oliver and Haeb-Umbach, Reinhold and Chaudhuri, Sourish and Raj,
    Bhiksha}, year={2013} }'
  chicago: Walter, Oliver, Reinhold Haeb-Umbach, Sourish Chaudhuri, and Bhiksha Raj.
    “Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language
    Modeling.” In <i>IEEE International Conference on Robotics and Automation (ICRA
    2013)</i>, 2013.
  ieee: O. Walter, R. Haeb-Umbach, S. Chaudhuri, and B. Raj, “Unsupervised Word Discovery
    from Phonetic Input Using Nested Pitman-Yor Language Modeling,” in <i>IEEE International
    Conference on Robotics and Automation (ICRA 2013)</i>, 2013.
  mla: Walter, Oliver, et al. “Unsupervised Word Discovery from Phonetic Input Using
    Nested Pitman-Yor Language Modeling.” <i>IEEE International Conference on Robotics
    and Automation (ICRA 2013)</i>, 2013.
  short: 'O. Walter, R. Haeb-Umbach, S. Chaudhuri, B. Raj, in: IEEE International
    Conference on Robotics and Automation (ICRA 2013), 2013.'
date_created: 2019-07-12T05:30:50Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013.pdf
oa: '1'
publication: IEEE International Conference on Robotics and Automation (ICRA 2013)
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Poster.pdf
  - description: Spotlight
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/WaHaChRa2013_Spotlight.pdf
status: public
title: Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language
  Modeling
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11924'
author:
- first_name: Oliver
  full_name: Walter, Oliver
  last_name: Walter
- first_name: Timo
  full_name: Korthals, Timo
  last_name: Korthals
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
- first_name: Bhiksha
  full_name: Raj, Bhiksha
  last_name: Raj
citation:
  ama: 'Walter O, Korthals T, Haeb-Umbach R, Raj B. Hierarchical System for Word Discovery
    Exploiting DTW-Based Initialization. In: <i>Automatic Speech Recognition and Understanding
    Workshop (ASRU 2013)</i>. ; 2013.'
  apa: Walter, O., Korthals, T., Haeb-Umbach, R., &#38; Raj, B. (2013). Hierarchical
    System for Word Discovery Exploiting DTW-Based Initialization. In <i>Automatic
    Speech Recognition and Understanding Workshop (ASRU 2013)</i>.
  bibtex: '@inproceedings{Walter_Korthals_Haeb-Umbach_Raj_2013, title={Hierarchical
    System for Word Discovery Exploiting DTW-Based Initialization}, booktitle={Automatic
    Speech Recognition and Understanding Workshop (ASRU 2013)}, author={Walter, Oliver
    and Korthals, Timo and Haeb-Umbach, Reinhold and Raj, Bhiksha}, year={2013} }'
  chicago: Walter, Oliver, Timo Korthals, Reinhold Haeb-Umbach, and Bhiksha Raj. “Hierarchical
    System for Word Discovery Exploiting DTW-Based Initialization.” In <i>Automatic
    Speech Recognition and Understanding Workshop (ASRU 2013)</i>, 2013.
  ieee: O. Walter, T. Korthals, R. Haeb-Umbach, and B. Raj, “Hierarchical System for
    Word Discovery Exploiting DTW-Based Initialization,” in <i>Automatic Speech Recognition
    and Understanding Workshop (ASRU 2013)</i>, 2013.
  mla: Walter, Oliver, et al. “Hierarchical System for Word Discovery Exploiting DTW-Based
    Initialization.” <i>Automatic Speech Recognition and Understanding Workshop (ASRU
    2013)</i>, 2013.
  short: 'O. Walter, T. Korthals, R. Haeb-Umbach, B. Raj, in: Automatic Speech Recognition
    and Understanding Workshop (ASRU 2013), 2013.'
date_created: 2019-07-12T05:30:53Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13.pdf
oa: '1'
publication: Automatic Speech Recognition and Understanding Workshop (ASRU 2013)
related_material:
  link:
  - description: Award
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Award.pdf
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/WaKoHaRa13_Poster.pdf
status: public
title: Hierarchical System for Word Discovery Exploiting DTW-Based Initialization
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11926'
abstract:
- lang: eng
  text: In this paper we present a novel initialization method for unsupervised learning
    of acoustic patterns in recordings of continuous speech. The pattern discovery
    task is solved by dynamic time warping whose performance we improve by a smart
    starting point selection. This enables a more accurate discovery of patterns compared
    to conventional approaches. After graph-based clustering the patterns are employed
    for training hidden Markov models for an unsupervised speech acquisition. By iterating
    between model training and decoding in an EM-like framework the word accuracy
    is continuously improved. On the TIDIGITS corpus we achieve a word error rate
    of about 13 percent by the proposed unsupervised pattern discovery approach, which
    neither assumes knowledge of the acoustic units nor of the labels of the training
    data.
author:
- first_name: Oliver
  full_name: Walter, Oliver
  last_name: Walter
- 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: Walter O, Schmalenstroeer J, Haeb-Umbach R. <i>A Novel Initialization Method
    for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>.;
    2013.
  apa: Walter, O., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2013). <i>A Novel Initialization
    Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>.
  bibtex: '@book{Walter_Schmalenstroeer_Haeb-Umbach_2013, title={A Novel Initialization
    Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)},
    author={Walter, Oliver and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold},
    year={2013} }'
  chicago: Walter, Oliver, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. <i>A Novel
    Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech
    (FGNT-2013-01)</i>, 2013.
  ieee: O. Walter, J. Schmalenstroeer, and R. Haeb-Umbach, <i>A Novel Initialization
    Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01)</i>.
    2013.
  mla: Walter, Oliver, et al. <i>A Novel Initialization Method for Unsupervised Learning
    of Acoustic Patterns in Speech (FGNT-2013-01)</i>. 2013.
  short: O. Walter, J. Schmalenstroeer, R. Haeb-Umbach, A Novel Initialization Method
    for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01), 2013.
date_created: 2019-07-12T05:30:55Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/WaScHa2013.pdf
oa: '1'
status: public
title: A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns
  in Speech (FGNT-2013-01)
type: report
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'
...
---
_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'
...
---
_id: '11818'
abstract:
- lang: eng
  text: In this paper we present a system for indoor navigation based on received
    signal strength index information of Wireless-LAN access points and relative position
    estimates. The relative position information is gathered from inertial smartphone
    sensors using a step detection and an orientation estimate. Our map data is hosted
    on a server employing a map renderer and a SQL database. The database includes
    a complete multilevel office building, within which the user can navigate. During
    navigation, the client retrieves the position estimate from the server, together
    with the corresponding map tiles to visualize the user's position on the smartphone
    display.
author:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Sarah
  full_name: Schmitz, Sarah
  last_name: Schmitz
- first_name: Christian
  full_name: Drueke, Christian
  last_name: Drueke
- first_name: Dang Hai Tran
  full_name: Vu, Dang Hai Tran
  last_name: Vu
- 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: 'Hoang MK, Schmitz S, Drueke C, Vu DHT, Schmalenstroeer J, Haeb-Umbach R. Server
    based indoor navigation using RSSI and inertial sensor information. In: <i>Positioning
    Navigation and Communication (WPNC), 2013 10th Workshop On</i>. ; 2013:1-6. doi:<a
    href="https://doi.org/10.1109/WPNC.2013.6533263">10.1109/WPNC.2013.6533263</a>'
  apa: Hoang, M. K., Schmitz, S., Drueke, C., Vu, D. H. T., Schmalenstroeer, J., &#38;
    Haeb-Umbach, R. (2013). Server based indoor navigation using RSSI and inertial
    sensor information. <i>Positioning Navigation and Communication (WPNC), 2013 10th
    Workshop On</i>, 1–6. <a href="https://doi.org/10.1109/WPNC.2013.6533263">https://doi.org/10.1109/WPNC.2013.6533263</a>
  bibtex: '@inproceedings{Hoang_Schmitz_Drueke_Vu_Schmalenstroeer_Haeb-Umbach_2013,
    title={Server based indoor navigation using RSSI and inertial sensor information},
    DOI={<a href="https://doi.org/10.1109/WPNC.2013.6533263">10.1109/WPNC.2013.6533263</a>},
    booktitle={Positioning Navigation and Communication (WPNC), 2013 10th Workshop
    on}, author={Hoang, Manh Kha and Schmitz, Sarah and Drueke, Christian and Vu,
    Dang Hai Tran and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2013},
    pages={1–6} }'
  chicago: Hoang, Manh Kha, Sarah Schmitz, Christian Drueke, Dang Hai Tran Vu, Joerg
    Schmalenstroeer, and Reinhold Haeb-Umbach. “Server Based Indoor Navigation Using
    RSSI and Inertial Sensor Information.” In <i>Positioning Navigation and Communication
    (WPNC), 2013 10th Workshop On</i>, 1–6, 2013. <a href="https://doi.org/10.1109/WPNC.2013.6533263">https://doi.org/10.1109/WPNC.2013.6533263</a>.
  ieee: 'M. K. Hoang, S. Schmitz, C. Drueke, D. H. T. Vu, J. Schmalenstroeer, and
    R. Haeb-Umbach, “Server based indoor navigation using RSSI and inertial sensor
    information,” in <i>Positioning Navigation and Communication (WPNC), 2013 10th
    Workshop on</i>, 2013, pp. 1–6, doi: <a href="https://doi.org/10.1109/WPNC.2013.6533263">10.1109/WPNC.2013.6533263</a>.'
  mla: Hoang, Manh Kha, et al. “Server Based Indoor Navigation Using RSSI and Inertial
    Sensor Information.” <i>Positioning Navigation and Communication (WPNC), 2013
    10th Workshop On</i>, 2013, pp. 1–6, doi:<a href="https://doi.org/10.1109/WPNC.2013.6533263">10.1109/WPNC.2013.6533263</a>.
  short: 'M.K. Hoang, S. Schmitz, C. Drueke, D.H.T. Vu, J. Schmalenstroeer, R. Haeb-Umbach,
    in: Positioning Navigation and Communication (WPNC), 2013 10th Workshop On, 2013,
    pp. 1–6.'
date_created: 2019-07-12T05:28:51Z
date_updated: 2023-10-26T08:09:36Z
department:
- _id: '54'
doi: 10.1109/WPNC.2013.6533263
keyword:
- SQL
- navigation
- smart phones
- wireless LAN
- RSSI
- SQL database
- complete multilevel office building
- inertial sensor information
- inertial smartphone sensors
- map renderer
- received signal strength index information
- relative position estimates
- server based indoor navigation
- step detection
- wireless-LAN access points
- Smartphone
- fingerprint
- indoor navigation
- map tile
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013.pdf
oa: '1'
page: 1-6
publication: Positioning Navigation and Communication (WPNC), 2013 10th Workshop on
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrScHa2013_Poster.pdf
status: public
title: Server based indoor navigation using RSSI and inertial sensor information
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11817'
abstract:
- lang: eng
  text: In this paper we present a modified hidden Markov model (HMM) for the fusion
    of received signal strength index (RSSI) information of WiFi access points and
    relative position information which is obtained from the inertial sensors of a
    smartphone for indoor positioning. Since the states of the HMM represent the potential
    user locations, their number determines the quantization error introduced by discretizing
    the allowable user positions through the use of the HMM. To reduce this quantization
    error we introduce â??pseudoâ?? states, whose emission probability, which models
    the RSSI measurements at this location, is synthesized from those of the neighboring
    states of which a Gaussian emission probability has been estimated during the
    training phase. The experimental results demonstrate the effectiveness of this
    approach. By introducing on average two pseudo states per original HMM state the
    positioning error could be significantly reduced without increasing the training
    effort.
author:
- first_name: Manh Kha
  full_name: Hoang, Manh Kha
  last_name: Hoang
- first_name: Joerg
  full_name: Schmalenstroeer, Joerg
  id: '460'
  last_name: Schmalenstroeer
- first_name: Christian
  full_name: Drueke, Christian
  last_name: Drueke
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Hoang MK, Schmalenstroeer J, Drueke C, Tran Vu DH, Haeb-Umbach R. A Hidden
    Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and Step Detection.
    In: <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>. ; 2013.'
  apa: Hoang, M. K., Schmalenstroeer, J., Drueke, C., Tran Vu, D. H., &#38; Haeb-Umbach,
    R. (2013). A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting
    and Step Detection. <i>21th European Signal Processing Conference (EUSIPCO 2013)</i>.
  bibtex: '@inproceedings{Hoang_Schmalenstroeer_Drueke_Tran Vu_Haeb-Umbach_2013, title={A
    Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and
    Step Detection}, booktitle={21th European Signal Processing Conference (EUSIPCO
    2013)}, author={Hoang, Manh Kha and Schmalenstroeer, Joerg and Drueke, Christian
    and Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2013} }'
  chicago: Hoang, Manh Kha, Joerg Schmalenstroeer, Christian Drueke, Dang Hai Tran
    Vu, and Reinhold Haeb-Umbach. “A Hidden Markov Model for Indoor User Tracking
    Based on WiFi Fingerprinting and Step Detection.” In <i>21th European Signal Processing
    Conference (EUSIPCO 2013)</i>, 2013.
  ieee: M. K. Hoang, J. Schmalenstroeer, C. Drueke, D. H. Tran Vu, and R. Haeb-Umbach,
    “A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting and
    Step Detection,” 2013.
  mla: Hoang, Manh Kha, et al. “A Hidden Markov Model for Indoor User Tracking Based
    on WiFi Fingerprinting and Step Detection.” <i>21th European Signal Processing
    Conference (EUSIPCO 2013)</i>, 2013.
  short: 'M.K. Hoang, J. Schmalenstroeer, C. Drueke, D.H. Tran Vu, R. Haeb-Umbach,
    in: 21th European Signal Processing Conference (EUSIPCO 2013), 2013.'
date_created: 2019-07-12T05:28:50Z
date_updated: 2023-10-26T08:09:45Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013.pdf
oa: '1'
publication: 21th European Signal Processing Conference (EUSIPCO 2013)
quality_controlled: '1'
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2013/HoScDrTrHa2013_Poster.pdf
status: public
title: A Hidden Markov Model for Indoor User Tracking Based on WiFi Fingerprinting
  and Step Detection
type: conference
user_id: '460'
year: '2013'
...
---
_id: '11741'
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. Quality Analysis and Optimization of the MAP-based
    Noise Power Spectral Density Tracker. In: <i>Speech Communication; 10. ITG Symposium;
    Proceedings.</i> ; 2012.'
  apa: Chinaev, A., &#38; Haeb-Umbach, R. (2012). Quality Analysis and Optimization
    of the MAP-based Noise Power Spectral Density Tracker. In <i>Speech Communication;
    10. ITG Symposium; Proceedings.</i>
  bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2012, title={Quality Analysis and Optimization
    of the MAP-based Noise Power Spectral Density Tracker}, booktitle={Speech Communication;
    10. ITG Symposium; Proceedings.}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold},
    year={2012} }'
  chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization
    of the MAP-Based Noise Power Spectral Density Tracker.” In <i>Speech Communication;
    10. ITG Symposium; Proceedings.</i>, 2012.
  ieee: A. Chinaev and R. Haeb-Umbach, “Quality Analysis and Optimization of the MAP-based
    Noise Power Spectral Density Tracker,” in <i>Speech Communication; 10. ITG Symposium;
    Proceedings.</i>, 2012.
  mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “Quality Analysis and Optimization
    of the MAP-Based Noise Power Spectral Density Tracker.” <i>Speech Communication;
    10. ITG Symposium; Proceedings.</i>, 2012.
  short: 'A. Chinaev, R. Haeb-Umbach, in: Speech Communication; 10. ITG Symposium;
    Proceedings., 2012.'
date_created: 2019-07-12T05:27:22Z
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/2012/ChHa12.pdf
oa: '1'
publication: Speech Communication; 10. ITG Symposium; Proceedings.
related_material:
  link:
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2012/ChHa12_Poster.pdf
status: public
title: Quality Analysis and Optimization of the MAP-based Noise Power Spectral Density
  Tracker
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11745'
abstract:
- lang: eng
  text: In this paper we present a novel noise power spectral density tracking algorithm
    and its use in single-channel speech enhancement. It has the unique feature that
    it is able to track the noise statistics even if speech is dominant in a given
    time-frequency bin. As a consequence it can follow non-stationary noise superposed
    by speech, even in the critical case of rising noise power. The algorithm requires
    an initial estimate of the power spectrum of speech and is thus meant to be used
    as a postprocessor to a first speech enhancement stage. An experimental comparison
    with a state-of-the-art noise tracking algorithm demonstrates lower estimation
    errors under low SNR conditions and smaller fluctuations of the estimated values,
    resulting in improved speech quality as measured by PESQ scores.
author:
- first_name: Aleksej
  full_name: Chinaev, Aleksej
  last_name: Chinaev
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Chinaev A, Krueger A, Tran Vu DH, Haeb-Umbach R. Improved Noise Power Spectral
    Density Tracking by a MAP-based Postprocessor. In: <i>37th International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>. ; 2012.'
  apa: Chinaev, A., Krueger, A., Tran Vu, D. H., &#38; Haeb-Umbach, R. (2012). Improved
    Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In <i>37th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>.
  bibtex: '@inproceedings{Chinaev_Krueger_Tran Vu_Haeb-Umbach_2012, title={Improved
    Noise Power Spectral Density Tracking by a MAP-based Postprocessor}, booktitle={37th
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)},
    author={Chinaev, Aleksej and Krueger, Alexander and Tran Vu, Dang Hai and Haeb-Umbach,
    Reinhold}, year={2012} }'
  chicago: Chinaev, Aleksej, Alexander Krueger, Dang Hai Tran Vu, and Reinhold Haeb-Umbach.
    “Improved Noise Power Spectral Density Tracking by a MAP-Based Postprocessor.”
    In <i>37th International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2012)</i>, 2012.
  ieee: A. Chinaev, A. Krueger, D. H. Tran Vu, and R. Haeb-Umbach, “Improved Noise
    Power Spectral Density Tracking by a MAP-based Postprocessor,” in <i>37th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2012)</i>, 2012.
  mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by
    a MAP-Based Postprocessor.” <i>37th International Conference on Acoustics, Speech
    and Signal Processing (ICASSP 2012)</i>, 2012.
  short: 'A. Chinaev, A. Krueger, D.H. Tran Vu, R. Haeb-Umbach, in: 37th International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.'
date_created: 2019-07-12T05:27:26Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- MAP parameter estimation
- noise power estimation
- speech enhancement
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12.pdf
oa: '1'
publication: 37th International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2012)
related_material:
  link:
  - description: Presentation
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2012/ChKrDaHa12_Talk.pdf
status: public
title: Improved Noise Power Spectral Density Tracking by a MAP-based Postprocessor
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11844'
author:
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Krueger A, Haeb-Umbach R. Reverberant Speech Recognition. In: <i>Techniques
    for Noise Robustness in Automatic Speech Recognition</i>. Wiley; 2012.'
  apa: Krueger, A., &#38; Haeb-Umbach, R. (2012). Reverberant Speech Recognition.
    In <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>. Wiley.
  bibtex: '@inbook{Krueger_Haeb-Umbach_2012, title={Reverberant Speech Recognition},
    booktitle={Techniques for Noise Robustness in Automatic Speech Recognition}, publisher={Wiley},
    author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.”
    In <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>. Wiley,
    2012.
  ieee: A. Krueger and R. Haeb-Umbach, “Reverberant Speech Recognition,” in <i>Techniques
    for Noise Robustness in Automatic Speech Recognition</i>, Wiley, 2012.
  mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Reverberant Speech Recognition.”
    <i>Techniques for Noise Robustness in Automatic Speech Recognition</i>, Wiley,
    2012.
  short: 'A. Krueger, R. Haeb-Umbach, in: Techniques for Noise Robustness in Automatic
    Speech Recognition, Wiley, 2012.'
date_created: 2019-07-12T05:29:21Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
publication: Techniques for Noise Robustness in Automatic Speech Recognition
publisher: Wiley
status: public
title: Reverberant Speech Recognition
type: book_chapter
user_id: '44006'
year: '2012'
...
---
_id: '11849'
abstract:
- lang: eng
  text: In this contribution we investigate the effectiveness of Bayesian feature
    enhancement (BFE) on a medium-sized recognition task containing real-world recordings
    of noisy reverberant speech. BFE employs a very coarse model of the acoustic impulse
    response (AIR) from the source to the microphone, which has been shown to be effective
    if the speech to be recognized has been generated by artificially convolving nonreverberant
    speech with a constant AIR. Here we demonstrate that the model is also appropriate
    to be used in feature enhancement of true recordings of noisy reverberant speech.
    On the Multi-Channel Wall Street Journal Audio Visual corpus (MC-WSJ-AV) the word
    error rate is cut in half to 41.9 percent compared to the ETSI Standard Front-End
    using as input the signal of a single distant microphone with a single recognition
    pass.
author:
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Oliver
  full_name: Walter, Oliver
  last_name: Walter
- 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: 'Krueger A, Walter O, Leutnant V, Haeb-Umbach R. Bayesian Feature Enhancement
    for ASR of Noisy Reverberant Real-World Data. In: <i>Proc. Interspeech</i>. Portland,
    USA; 2012.'
  apa: Krueger, A., Walter, O., Leutnant, V., &#38; Haeb-Umbach, R. (2012). Bayesian
    Feature Enhancement for ASR of Noisy Reverberant Real-World Data. In <i>Proc.
    Interspeech</i>. Portland, USA.
  bibtex: '@inproceedings{Krueger_Walter_Leutnant_Haeb-Umbach_2012, place={Portland,
    USA}, title={Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World
    Data}, booktitle={Proc. Interspeech}, author={Krueger, Alexander and Walter, Oliver
    and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Krueger, Alexander, Oliver Walter, Volker Leutnant, and Reinhold Haeb-Umbach.
    “Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data.” In
    <i>Proc. Interspeech</i>. Portland, USA, 2012.
  ieee: A. Krueger, O. Walter, V. Leutnant, and R. Haeb-Umbach, “Bayesian Feature
    Enhancement for ASR of Noisy Reverberant Real-World Data,” in <i>Proc. Interspeech</i>,
    2012.
  mla: Krueger, Alexander, et al. “Bayesian Feature Enhancement for ASR of Noisy Reverberant
    Real-World Data.” <i>Proc. Interspeech</i>, 2012.
  short: 'A. Krueger, O. Walter, V. Leutnant, R. Haeb-Umbach, in: Proc. Interspeech,
    Portland, USA, 2012.'
date_created: 2019-07-12T05:29:27Z
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/2012/KrWaLeHa2012.pdf
oa: '1'
place: Portland, USA
publication: Proc. Interspeech
status: public
title: Bayesian Feature Enhancement for ASR of Noisy Reverberant Real-World Data
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11863'
abstract:
- lang: eng
  text: 'In this contribution, a new observation model for the joint compensation
    of reverberation and noise in the logarithmic mel power spectral density domain
    will be considered. The proposed observation model relates the noisy reverberant
    feature to the underlying sequence of clean speech features and the feature of
    the noise. Nevertheless, due to the complex interaction of these variables in
    the target domain, the observationmodel cannot be applied to Bayesian feature
    enhancement directly, calling for approximations that eventually render the observation
    model useful. The performance of the approximated observation model will highly
    depend on the capability of modeling the difference between the model and the
    noisy reverberant observation. A detailed analysis of this observation error will
    be provided in this work. Among others, it will point out the need to account
    for the instantaneous ratio of the reverberant speech power and the noise power.
    Index Terms: Bayesian feature enhancement, observation model for noisy reverberant
    speech'
author:
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Leutnant V, Krueger A, Haeb-Umbach R. Investigations Into a Statistical Observation
    Model for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant
    Speech. <i>Speech Communication; 10 ITG Symposium; Proceedings of</i>. 2012:1-4.
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). Investigations Into
    a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features
    of Noisy Reverberant Speech. <i>Speech Communication; 10. ITG Symposium; Proceedings
    Of</i>, 1–4.
  bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2012, title={Investigations Into
    a Statistical Observation Model for Logarithmic Mel Power Spectral Density Features
    of Noisy Reverberant Speech}, journal={Speech Communication; 10. ITG Symposium;
    Proceedings of}, author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach,
    Reinhold}, year={2012}, pages={1–4} }'
  chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “Investigations
    Into a Statistical Observation Model for Logarithmic Mel Power Spectral Density
    Features of Noisy Reverberant Speech.” <i>Speech Communication; 10. ITG Symposium;
    Proceedings Of</i>, 2012, 1–4.
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Investigations Into a Statistical
    Observation Model for Logarithmic Mel Power Spectral Density Features of Noisy
    Reverberant Speech,” <i>Speech Communication; 10. ITG Symposium; Proceedings of</i>,
    pp. 1–4, 2012.
  mla: Leutnant, Volker, et al. “Investigations Into a Statistical Observation Model
    for Logarithmic Mel Power Spectral Density Features of Noisy Reverberant Speech.”
    <i>Speech Communication; 10. ITG Symposium; Proceedings Of</i>, 2012, pp. 1–4.
  short: V. Leutnant, A. Krueger, R. Haeb-Umbach, Speech Communication; 10. ITG Symposium;
    Proceedings Of (2012) 1–4.
date_created: 2019-07-12T05:29:43Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6309628
oa: '1'
page: 1-4
publication: Speech Communication; 10. ITG Symposium; Proceedings of
status: public
title: Investigations Into a Statistical Observation Model for Logarithmic Mel Power
  Spectral Density Features of Noisy Reverberant Speech
type: journal_article
user_id: '44006'
year: '2012'
...
---
_id: '11864'
abstract:
- lang: eng
  text: In this work, an observation model for the joint compensation of noise and
    reverberation in the logarithmic mel power spectral density domain is considered.
    It relates the features of the noisy reverberant speech to those of the non-reverberant
    speech and the noise. In contrast to enhancement of features only corrupted by
    reverberation (reverberant features), enhancement of noisy reverberant features
    requires a more sophisticated model for the error introduced by the proposed observation
    model. In a first consideration, it will be shown that this error is highly dependent
    on the instantaneous ratio of the power of reverberant speech to the power of
    the noise and, moreover, sensitive to the phase between reverberant speech and
    noise in the short-time discrete Fourier domain. Afterwards, a statistically motivated
    approach will be presented allowing for the model of the observation error to
    be inferred from the error model previously used for the reverberation only case.
    Finally, the developed observation error model will be utilized in a Bayesian
    feature enhancement scheme, leading to improvements in word accuracy on the AURORA5
    database.
author:
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Leutnant V, Krueger A, Haeb-Umbach R. A Statistical Observation Model For
    Noisy Reverberant Speech Features and its Application to Robust ASR. In: <i>Signal
    Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
    On</i>. ; 2012.'
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). A Statistical Observation
    Model For Noisy Reverberant Speech Features and its Application to Robust ASR.
    In <i>Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
    Conference on</i>.
  bibtex: '@inproceedings{Leutnant_Krueger_Haeb-Umbach_2012, title={A Statistical
    Observation Model For Noisy Reverberant Speech Features and its Application to
    Robust ASR}, booktitle={Signal Processing, Communications and Computing (ICSPCC),
    2012 IEEE International Conference on}, author={Leutnant, Volker and Krueger,
    Alexander and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A Statistical
    Observation Model For Noisy Reverberant Speech Features and Its Application to
    Robust ASR.” In <i>Signal Processing, Communications and Computing (ICSPCC), 2012
    IEEE International Conference On</i>, 2012.
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A Statistical Observation Model
    For Noisy Reverberant Speech Features and its Application to Robust ASR,” in <i>Signal
    Processing, Communications and Computing (ICSPCC), 2012 IEEE International Conference
    on</i>, 2012.
  mla: Leutnant, Volker, et al. “A Statistical Observation Model For Noisy Reverberant
    Speech Features and Its Application to Robust ASR.” <i>Signal Processing, Communications
    and Computing (ICSPCC), 2012 IEEE International Conference On</i>, 2012.
  short: 'V. Leutnant, A. Krueger, R. Haeb-Umbach, in: Signal Processing, Communications
    and Computing (ICSPCC), 2012 IEEE International Conference On, 2012.'
date_created: 2019-07-12T05:29:44Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
keyword:
- Robust Automatic Speech Recognition
- Bayesian feature enhancement
- observation model for reverberant and noisy speech
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6335731
oa: '1'
publication: Signal Processing, Communications and Computing (ICSPCC), 2012 IEEE International
  Conference on
status: public
title: A Statistical Observation Model For Noisy Reverberant Speech Features and its
  Application to Robust ASR
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11865'
author:
- first_name: Volker
  full_name: Leutnant, Volker
  last_name: Leutnant
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Leutnant V, Krueger A, Haeb-Umbach R. <i>Derivation of the Power Compensation
    Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel
    Power Spectral Domain</i>.; 2012.
  apa: Leutnant, V., Krueger, A., &#38; Haeb-Umbach, R. (2012). <i>Derivation of the
    Power Compensation Constant in the Observation Model for Reverberant Speech in
    the Logarithmic Mel Power Spectral Domain</i>.
  bibtex: '@book{Leutnant_Krueger_Haeb-Umbach_2012, title={Derivation of the Power
    Compensation Constant in the Observation Model for Reverberant Speech in the Logarithmic
    Mel Power Spectral Domain}, author={Leutnant, Volker and Krueger, Alexander and
    Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. <i>Derivation
    of the Power Compensation Constant in the Observation Model for Reverberant Speech
    in the Logarithmic Mel Power Spectral Domain</i>, 2012.
  ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, <i>Derivation of the Power Compensation
    Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel
    Power Spectral Domain</i>. 2012.
  mla: Leutnant, Volker, et al. <i>Derivation of the Power Compensation Constant in
    the Observation Model for Reverberant Speech in the Logarithmic Mel Power Spectral
    Domain</i>. 2012.
  short: V. Leutnant, A. Krueger, R. Haeb-Umbach, Derivation of the Power Compensation
    Constant in the Observation Model for Reverberant Speech in the Logarithmic Mel
    Power Spectral Domain, 2012.
date_created: 2019-07-12T05:29:45Z
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/2012/LeuKruHab2012c.pdf
oa: '1'
status: public
title: Derivation of the Power Compensation Constant in the Observation Model for
  Reverberant Speech in the Logarithmic Mel Power Spectral Domain
type: report
user_id: '44006'
year: '2012'
...
---
_id: '11910'
author:
- first_name: Dang Hai
  full_name: Tran Vu, Dang Hai
  last_name: Tran Vu
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Tran Vu DH, Haeb-Umbach R. Exploiting Temporal Correlations in Joint Multichannel
    Speech Separation and Noise Suppression using Hidden Markov Models. In: <i>International
    Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>. ; 2012.'
  apa: Tran Vu, D. H., &#38; Haeb-Umbach, R. (2012). Exploiting Temporal Correlations
    in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov
    Models. In <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>.
  bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2012, title={Exploiting Temporal Correlations
    in Joint Multichannel Speech Separation and Noise Suppression using Hidden Markov
    Models}, booktitle={International Workshop on Acoustic Signal Enhancement (IWAENC2012)},
    author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Exploiting Temporal Correlations
    in Joint Multichannel Speech Separation and Noise Suppression Using Hidden Markov
    Models.” In <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>,
    2012.
  ieee: D. H. Tran Vu and R. Haeb-Umbach, “Exploiting Temporal Correlations in Joint
    Multichannel Speech Separation and Noise Suppression using Hidden Markov Models,”
    in <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>,
    2012.
  mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Exploiting Temporal Correlations
    in Joint Multichannel Speech Separation and Noise Suppression Using Hidden Markov
    Models.” <i>International Workshop on Acoustic Signal Enhancement (IWAENC2012)</i>,
    2012.
  short: 'D.H. Tran Vu, R. Haeb-Umbach, in: International Workshop on Acoustic Signal
    Enhancement (IWAENC2012), 2012.'
date_created: 2019-07-12T05:30:37Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
language:
- iso: eng
publication: International Workshop on Acoustic Signal Enhancement (IWAENC2012)
status: public
title: Exploiting Temporal Correlations in Joint Multichannel Speech Separation and
  Noise Suppression using Hidden Markov Models
type: conference
user_id: '44006'
year: '2012'
...
---
_id: '11833'
abstract:
- lang: eng
  text: In this paper we propose an approach to retrieve the geometry of an acoustic
    sensor network consisting of spatially distributed microphone arrays from unconstrained
    speech input. The calibration relies on Direction of Arrival (DoA) measurements
    which do not require a clock synchronization among the sensor nodes. The calibration
    problem is formulated as a cost function optimization task, which minimizes the
    squared differences between measured and predicted observations and additionally
    avoids the existence of minima that correspond to mirrored versions of the actual
    sensor orientations. Further, outlier measurements caused by reverberation are
    mitigated by a Random Sample Consensus (RANSAC) approach. The experimental results
    show a mean positioning error of at most 25 cm even in highly reverberant environments.
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. Microphone Array Position Self-Calibration
    from Reverberant Speech Input. In: <i>International Workshop on Acoustic Signal
    Enhancement (IWAENC 2012)</i>. ; 2012.'
  apa: Jacob, F., Schmalenstroeer, J., &#38; Haeb-Umbach, R. (2012). Microphone Array
    Position Self-Calibration from Reverberant Speech Input. <i>International Workshop
    on Acoustic Signal Enhancement (IWAENC 2012)</i>.
  bibtex: '@inproceedings{Jacob_Schmalenstroeer_Haeb-Umbach_2012, title={Microphone
    Array Position Self-Calibration from Reverberant Speech Input}, booktitle={International
    Workshop on Acoustic Signal Enhancement (IWAENC 2012)}, author={Jacob, Florian
    and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2012} }'
  chicago: Jacob, Florian, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Microphone
    Array Position Self-Calibration from Reverberant Speech Input.” In <i>International
    Workshop on Acoustic Signal Enhancement (IWAENC 2012)</i>, 2012.
  ieee: F. Jacob, J. Schmalenstroeer, and R. Haeb-Umbach, “Microphone Array Position
    Self-Calibration from Reverberant Speech Input,” 2012.
  mla: Jacob, Florian, et al. “Microphone Array Position Self-Calibration from Reverberant
    Speech Input.” <i>International Workshop on Acoustic Signal Enhancement (IWAENC
    2012)</i>, 2012.
  short: 'F. Jacob, J. Schmalenstroeer, R. Haeb-Umbach, in: International Workshop
    on Acoustic Signal Enhancement (IWAENC 2012), 2012.'
date_created: 2019-07-12T05:29:08Z
date_updated: 2023-10-26T08:10:52Z
department:
- _id: '54'
keyword:
- Unsupervised
- geometry calibration
- microphone arrays
- position self-calibration
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12.pdf
oa: '1'
publication: International Workshop on Acoustic Signal Enhancement (IWAENC 2012)
quality_controlled: '1'
related_material:
  link:
  - description: Video
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2012/Microphine_Array_Position_Self-Calibration_from_Reverberant_Speech_Input.mp4
  - description: Poster
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Poster.pdf
  - description: Demonstrator
    relation: supplementary_material
    url: https://groups.uni-paderborn.de/nt/pubs/2012/JaScHa12_Demonstrator.pdf
status: public
title: Microphone Array Position Self-Calibration from Reverberant Speech Input
type: conference
user_id: '460'
year: '2012'
...