---
_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: '11850'
abstract:
- lang: eng
  text: In this paper, we present a novel blocking matrix and fixed beamformer design
    for a generalized sidelobe canceler for speech enhancement in a reverberant enclosure.
    They are based on a new method for estimating the acoustical transfer function
    ratios in the presence of stationary noise. The estimation method relies on solving
    a generalized eigenvalue problem in each frequency bin. An adaptive eigenvector
    tracking utilizing the power iteration method is employed and shown to achieve
    a high convergence speed. Simulation results demonstrate that the proposed beamformer
    leads to better noise and interference reduction and reduced speech distortions
    compared to other blocking matrix designs from the literature.
author:
- first_name: Alexander
  full_name: Krueger, Alexander
  last_name: Krueger
- first_name: Ernst
  full_name: Warsitz, Ernst
  last_name: Warsitz
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Krueger A, Warsitz E, Haeb-Umbach R. Speech Enhancement With a GSC-Like Structure
    Employing Eigenvector-Based Transfer Function Ratios Estimation. <i>IEEE Transactions
    on Audio, Speech, and Language Processing</i>. 2011;19(1):206-219. doi:<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>
  apa: Krueger, A., Warsitz, E., &#38; Haeb-Umbach, R. (2011). Speech Enhancement
    With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
    Estimation. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    <i>19</i>(1), 206–219. <a href="https://doi.org/10.1109/TASL.2010.2047324">https://doi.org/10.1109/TASL.2010.2047324</a>
  bibtex: '@article{Krueger_Warsitz_Haeb-Umbach_2011, title={Speech Enhancement With
    a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios Estimation},
    volume={19}, DOI={<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>},
    number={1}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Krueger, Alexander and Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2011},
    pages={206–219} }'
  chicago: 'Krueger, Alexander, Ernst Warsitz, and Reinhold Haeb-Umbach. “Speech Enhancement
    With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
    Estimation.” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>
    19, no. 1 (2011): 206–19. <a href="https://doi.org/10.1109/TASL.2010.2047324">https://doi.org/10.1109/TASL.2010.2047324</a>.'
  ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like
    Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” <i>IEEE
    Transactions on Audio, Speech, and Language Processing</i>, vol. 19, no. 1, pp.
    206–219, 2011.
  mla: Krueger, Alexander, et al. “Speech Enhancement With a GSC-Like Structure Employing
    Eigenvector-Based Transfer Function Ratios Estimation.” <i>IEEE Transactions on
    Audio, Speech, and Language Processing</i>, vol. 19, no. 1, 2011, pp. 206–19,
    doi:<a href="https://doi.org/10.1109/TASL.2010.2047324">10.1109/TASL.2010.2047324</a>.
  short: A. Krueger, E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech,
    and Language Processing 19 (2011) 206–219.
date_created: 2019-07-12T05:29:28Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2047324
intvolume: '        19'
issue: '1'
keyword:
- acoustical transfer function ratio
- adaptive eigenvector tracking
- array signal processing
- beamformer design
- blocking matrix
- eigenvalues and eigenfunctions
- eigenvector-based transfer function ratios estimation
- generalized sidelobe canceler
- interference reduction
- iterative methods
- power iteration method
- reduced speech distortions
- reverberant enclosure
- reverberation
- speech enhancement
- stationary noise
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2011/KrWaHa11.pdf
oa: '1'
page: 206-219
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Speech Enhancement With a GSC-Like Structure Employing Eigenvector-Based Transfer
  Function Ratios Estimation
type: journal_article
user_id: '44006'
volume: 19
year: '2011'
...
---
_id: '11937'
abstract:
- lang: eng
  text: In automatic speech recognition, hidden Markov models (HMMs) are commonly
    used for speech decoding, while switching linear dynamic models (SLDMs) can be
    employed for a preceding model-based speech feature enhancement. In this paper,
    these model types are combined in order to obtain a novel iterative speech feature
    enhancement and recognition architecture. It is shown that speech feature enhancement
    with SLDMs can be improved by feeding back information from the HMM to the enhancement
    stage. Two different feedback structures are derived. In the first, the posteriors
    of the HMM states are used to control the model probabilities of the SLDMs, while
    in the second they are employed to directly influence the estimate of the speech
    feature distribution. Both approaches lead to improvements in recognition accuracy
    both on the AURORA2 and AURORA4 databases compared to non-iterative speech feature
    enhancement with SLDMs. It is also shown that a combination with uncertainty decoding
    further enhances performance.
author:
- first_name: Stefan
  full_name: Windmann, Stefan
  last_name: Windmann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: Windmann S, Haeb-Umbach R. Approaches to Iterative Speech Feature Enhancement
    and Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>.
    2009;17(5):974-984. doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2009). Approaches to Iterative Speech
    Feature Enhancement and Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, <i>17</i>(5), 974–984. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>
  bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Approaches to Iterative Speech
    Feature Enhancement and Recognition}, volume={17}, DOI={<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>},
    number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={974–984}
    }'
  chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i> 17, no. 5 (2009): 974–84. <a href="https://doi.org/10.1109/TASL.2009.2014894">https://doi.org/10.1109/TASL.2009.2014894</a>.'
  ieee: S. Windmann and R. Haeb-Umbach, “Approaches to Iterative Speech Feature Enhancement
    and Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    vol. 17, no. 5, pp. 974–984, 2009.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Approaches to Iterative Speech
    Feature Enhancement and Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, vol. 17, no. 5, 2009, pp. 974–84, doi:<a href="https://doi.org/10.1109/TASL.2009.2014894">10.1109/TASL.2009.2014894</a>.
  short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 17 (2009) 974–984.
date_created: 2019-07-12T05:31:08Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2014894
intvolume: '        17'
issue: '5'
keyword:
- AURORA2 databases
- AURORA4 databases
- automatic speech recognition
- feedback structures
- hidden Markov models
- HMM
- iterative methods
- iterative speech feature enhancement
- model probabilities
- speech decoding
- speech enhancement
- speech feature distribution
- speech recognition
- switching linear dynamic models
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-1.pdf
oa: '1'
page: 974-984
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Approaches to Iterative Speech Feature Enhancement and Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11943'
abstract:
- lang: eng
  text: A marginalized particle filter is proposed for performing single channel speech
    enhancement with a non-linear dynamic state model. The system consists of a particle
    filter for tracking line spectral pair (LSP) parameters and a Kalman filter per
    particle for speech enhancement. The state model for the LSPs has been learnt
    on clean speech training data. In our approach parameters and speech samples are
    processed at different time scales by assuming the parameters to be constant for
    small blocks of data. Further enhancement is obtained by an iteration which can
    be applied on these small blocks. The experiments show that similar SNR gains
    are obtained as with the Kalman-LM-iterative algorithm. However better values
    of the noise level and the log-spectral distance are achieved
author:
- first_name: Stefan
  full_name: Windmann, Stefan
  last_name: Windmann
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Windmann S, Haeb-Umbach R. Iterative Speech Enhancement using a Non-Linear
    Dynamic State Model of Speech and its Parameters. In: <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>. Vol 1. ; 2006:I.
    doi:<a href="https://doi.org/10.1109/ICASSP.2006.1660058">10.1109/ICASSP.2006.1660058</a>'
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2006). Iterative Speech Enhancement using
    a Non-Linear Dynamic State Model of Speech and its Parameters. In <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i> (Vol.
    1, p. I). <a href="https://doi.org/10.1109/ICASSP.2006.1660058">https://doi.org/10.1109/ICASSP.2006.1660058</a>
  bibtex: '@inproceedings{Windmann_Haeb-Umbach_2006, title={Iterative Speech Enhancement
    using a Non-Linear Dynamic State Model of Speech and its Parameters}, volume={1},
    DOI={<a href="https://doi.org/10.1109/ICASSP.2006.1660058">10.1109/ICASSP.2006.1660058</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2006)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2006},
    pages={I} }'
  chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement
    Using a Non-Linear Dynamic State Model of Speech and Its Parameters.” In <i>IEEE
    International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>,
    1:I, 2006. <a href="https://doi.org/10.1109/ICASSP.2006.1660058">https://doi.org/10.1109/ICASSP.2006.1660058</a>.
  ieee: S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear
    Dynamic State Model of Speech and its Parameters,” in <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, 2006, vol. 1, p.
    I.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Iterative Speech Enhancement Using
    a Non-Linear Dynamic State Model of Speech and Its Parameters.” <i>IEEE International
    Conference on Acoustics, Speech and Signal Processing (ICASSP 2006)</i>, vol.
    1, 2006, p. I, doi:<a href="https://doi.org/10.1109/ICASSP.2006.1660058">10.1109/ICASSP.2006.1660058</a>.
  short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2006), 2006, p. I.'
date_created: 2019-07-12T05:31:15Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2006.1660058
intvolume: '         1'
keyword:
- clean speech training data
- iterative methods
- iterative speech enhancement
- Kalman filter
- Kalman filters
- Kalman-LM-iterative algorithm
- line spectral pair parameters
- log-spectral distance
- marginalized particle filter
- noise level
- nonlinear dynamic state speech model
- particle filtering (numerical methods)
- single channel speech enhancement
- SNR gains
- speech enhancement
- speech samples
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2006/WiHa06-2.pdf
oa: '1'
page: I
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2006)
status: public
title: Iterative Speech Enhancement using a Non-Linear Dynamic State Model of Speech
  and its Parameters
type: conference
user_id: '44006'
volume: 1
year: '2006'
...
---
_id: '11930'
abstract:
- lang: eng
  text: For human-machine interfaces in distant-talking environments multichannel
    signal processing is often employed to obtain an enhanced signal for subsequent
    processing. In this paper we propose a novel adaptation algorithm for a filter-and-sum
    beamformer to adjust the coefficients of FIR filters to changing acoustic room
    impulses, e.g. due to speaker movement. A deterministic and a stochastic gradient
    ascent algorithm are derived from a constrained optimization problem, which iteratively
    estimates the eigenvector corresponding to the largest eigenvalue of the cross
    power spectral density of the microphone signals. The method does not require
    an explicit estimation of the speaker location. The experimental results show
    fast adaptation and excellent robustness of the proposed algorithm.
author:
- first_name: Ernst
  full_name: Warsitz, Ernst
  last_name: Warsitz
- first_name: Reinhold
  full_name: Haeb-Umbach, Reinhold
  id: '242'
  last_name: Haeb-Umbach
citation:
  ama: 'Warsitz E, Haeb-Umbach R. Acoustic filter-and-sum beamforming by adaptive
    principal component analysis. In: <i>IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2005)</i>. Vol 4. ; 2005:iv/797-iv/800 Vol.
    4. doi:<a href="https://doi.org/10.1109/ICASSP.2005.1416129">10.1109/ICASSP.2005.1416129</a>'
  apa: Warsitz, E., &#38; Haeb-Umbach, R. (2005). Acoustic filter-and-sum beamforming
    by adaptive principal component analysis. In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2005)</i> (Vol. 4, p. iv/797-iv/800
    Vol. 4). <a href="https://doi.org/10.1109/ICASSP.2005.1416129">https://doi.org/10.1109/ICASSP.2005.1416129</a>
  bibtex: '@inproceedings{Warsitz_Haeb-Umbach_2005, title={Acoustic filter-and-sum
    beamforming by adaptive principal component analysis}, volume={4}, DOI={<a href="https://doi.org/10.1109/ICASSP.2005.1416129">10.1109/ICASSP.2005.1416129</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2005)}, author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2005},
    pages={iv/797-iv/800 Vol. 4} }'
  chicago: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
    by Adaptive Principal Component Analysis.” In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, 4:iv/797-iv/800
    Vol. 4, 2005. <a href="https://doi.org/10.1109/ICASSP.2005.1416129">https://doi.org/10.1109/ICASSP.2005.1416129</a>.
  ieee: E. Warsitz and R. Haeb-Umbach, “Acoustic filter-and-sum beamforming by adaptive
    principal component analysis,” in <i>IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2005)</i>, 2005, vol. 4, p. iv/797-iv/800
    Vol. 4.
  mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Acoustic Filter-and-Sum Beamforming
    by Adaptive Principal Component Analysis.” <i>IEEE International Conference on
    Acoustics, Speech and Signal Processing (ICASSP 2005)</i>, vol. 4, 2005, p. iv/797-iv/800
    Vol. 4, doi:<a href="https://doi.org/10.1109/ICASSP.2005.1416129">10.1109/ICASSP.2005.1416129</a>.
  short: 'E. Warsitz, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2005), 2005, p. iv/797-iv/800 Vol. 4.'
date_created: 2019-07-12T05:31:00Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2005.1416129
intvolume: '         4'
keyword:
- acoustic filter-and-sum beamforming
- acoustic room impulses
- acoustic signal processing
- adaptive principal component analysis
- adaptive signal processing
- architectural acoustics
- constrained optimization problem
- cross power spectral density
- deterministic algorithm
- deterministic algorithms
- distant-talking environments
- eigenvalues and eigenfunctions
- eigenvector
- enhanced signal
- filter-and-sum beamformer
- FIR filter coefficients
- FIR filter coefficients
- FIR filters
- gradient methods
- human-machine interfaces
- iterative estimation
- iterative methods
- largest eigenvalue
- microphone signals
- multichannel signal processing
- optimisation
- principal component analysis
- spectral analysis
- stochastic gradient ascent algorithm
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2005/WaHa05.pdf
oa: '1'
page: iv/797-iv/800 Vol. 4
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2005)
status: public
title: Acoustic filter-and-sum beamforming by adaptive principal component analysis
type: conference
user_id: '44006'
volume: 4
year: '2005'
...