---
_id: '6075'
abstract:
- lang: eng
  text: For almost three decades, the theory of visual attention (TVA) has been successful
    in mathematically describing and explaining a wide variety of phenomena in visual
    selection and recognition with high quantitative precision. Interestingly, the
    influence of feature contrast on attention has been included in TVA only recently,
    although it has been extensively studied outside the TVA framework. The present
    approach further develops this extension of TVA’s scope by measuring and modeling
    salience. An empirical measure of salience is achieved by linking different (orientation
    and luminance) contrasts to a TVA parameter. In the modeling part, the function
    relating feature contrasts to salience is described mathematically and tested
    against alternatives by Bayesian model comparison. This model comparison reveals
    that the power function is an appropriate model of salience growth in the dimensions
    of orientation and luminance contrast. Furthermore, if contrasts from the two
    dimensions are comb
article_type: original
author:
- first_name: Alexander
  full_name: Krüger, Alexander
  last_name: Krüger
- first_name: Jan
  full_name: Tünnermann, Jan
  last_name: Tünnermann
- first_name: Ingrid
  full_name: Scharlau, Ingrid
  id: '451'
  last_name: Scharlau
  orcid: 0000-0003-2364-9489
citation:
  ama: Krüger A, Tünnermann J, Scharlau I. Measuring and modeling salience with the
    theory of visual attention. <i>Attention, Perception, &#38; Psychophysics</i>.
    2017;79(6):1593-1614. doi:<a href="https://doi.org/10.3758/s13414-017-1325-6">10.3758/s13414-017-1325-6</a>
  apa: Krüger, A., Tünnermann, J., &#38; Scharlau, I. (2017). Measuring and modeling
    salience with the theory of visual attention. <i>Attention, Perception, &#38;
    Psychophysics</i>, <i>79</i>(6), 1593–1614. <a href="https://doi.org/10.3758/s13414-017-1325-6">https://doi.org/10.3758/s13414-017-1325-6</a>
  bibtex: '@article{Krüger_Tünnermann_Scharlau_2017, title={Measuring and modeling
    salience with the theory of visual attention.}, volume={79}, DOI={<a href="https://doi.org/10.3758/s13414-017-1325-6">10.3758/s13414-017-1325-6</a>},
    number={6}, journal={Attention, Perception, &#38; Psychophysics}, author={Krüger,
    Alexander and Tünnermann, Jan and Scharlau, Ingrid}, year={2017}, pages={1593–1614}
    }'
  chicago: 'Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Measuring and
    Modeling Salience with the Theory of Visual Attention.” <i>Attention, Perception,
    &#38; Psychophysics</i> 79, no. 6 (2017): 1593–1614. <a href="https://doi.org/10.3758/s13414-017-1325-6">https://doi.org/10.3758/s13414-017-1325-6</a>.'
  ieee: 'A. Krüger, J. Tünnermann, and I. Scharlau, “Measuring and modeling salience
    with the theory of visual attention.,” <i>Attention, Perception, &#38; Psychophysics</i>,
    vol. 79, no. 6, pp. 1593–1614, 2017, doi: <a href="https://doi.org/10.3758/s13414-017-1325-6">10.3758/s13414-017-1325-6</a>.'
  mla: Krüger, Alexander, et al. “Measuring and Modeling Salience with the Theory
    of Visual Attention.” <i>Attention, Perception, &#38; Psychophysics</i>, vol.
    79, no. 6, 2017, pp. 1593–614, doi:<a href="https://doi.org/10.3758/s13414-017-1325-6">10.3758/s13414-017-1325-6</a>.
  short: A. Krüger, J. Tünnermann, I. Scharlau, Attention, Perception, &#38; Psychophysics
    79 (2017) 1593–1614.
date_created: 2018-12-10T07:05:04Z
date_updated: 2022-06-06T14:08:05Z
department:
- _id: '424'
doi: 10.3758/s13414-017-1325-6
intvolume: '        79'
issue: '6'
keyword:
- Salience
- Visual attention
- Bayesian inference
- Theory of visual attention
- Computational modeling
- Inference
- Object Recognition
- Theories
- Visual Perception
- Visual Attention
- Luminance
- Perceptual Orientation
- Statistical Probability
- Stimulus Salience
- Computational Modeling
language:
- iso: eng
page: 1593 - 1614
publication: Attention, Perception, & Psychophysics
publication_identifier:
  issn:
  - 1943-3921
publication_status: published
status: public
title: Measuring and modeling salience with the theory of visual attention.
type: journal_article
user_id: '42165'
volume: 79
year: '2017'
...
---
_id: '6071'
abstract:
- lang: eng
  text: Particular differences between an object and its surrounding cause salience,
    guide attention, and improve performance in various tasks. While much research
    has been dedicated to identifying which feature dimensions contribute to salience,
    much less regard has been paid to the quantitative strength of the salience caused
    by feature differences. Only a few studies systematically related salience effects
    to a common salience measure, and they are partly outdated in the light of new
    findings on the time course of salience effects. We propose Bundesen’s Theory
    of Visual Attention (TV A) as a theoretical basis for measuring salience and introduce
    an empirical and modeling approach to link this theory to data retrieved from
    temporal-order judgments. With this procedure, TV A becomes applicable to a broad
    range of salience-related stimulus material. Three experiments with orientation
    pop-out displays demonstrate the feasibility of the method. A 4th experiment substantiates
    its applicability t
author:
- first_name: Alexander
  full_name: Krüger, Alexander
  last_name: Krüger
- first_name: Jan
  full_name: Tünnermann, Jan
  last_name: Tünnermann
- first_name: Ingrid
  full_name: Scharlau, Ingrid
  id: '451'
  last_name: Scharlau
  orcid: 0000-0003-2364-9489
citation:
  ama: Krüger A, Tünnermann J, Scharlau I. Fast and conspicuous? Quantifying salience
    with the theory of visual attention. <i>Advances in Cognitive Psychology</i>.
    2016;12(1):20-38. doi:<a href="https://doi.org/10.5709/acp-0184-1">10.5709/acp-0184-1</a>
  apa: Krüger, A., Tünnermann, J., &#38; Scharlau, I. (2016). Fast and conspicuous?
    Quantifying salience with the theory of visual attention. <i>Advances in Cognitive
    Psychology</i>, <i>12</i>(1), 20–38. <a href="https://doi.org/10.5709/acp-0184-1">https://doi.org/10.5709/acp-0184-1</a>
  bibtex: '@article{Krüger_Tünnermann_Scharlau_2016, title={Fast and conspicuous?
    Quantifying salience with the theory of visual attention.}, volume={12}, DOI={<a
    href="https://doi.org/10.5709/acp-0184-1">10.5709/acp-0184-1</a>}, number={1},
    journal={Advances in Cognitive Psychology}, author={Krüger, Alexander and Tünnermann,
    Jan and Scharlau, Ingrid}, year={2016}, pages={20–38} }'
  chicago: 'Krüger, Alexander, Jan Tünnermann, and Ingrid Scharlau. “Fast and Conspicuous?
    Quantifying Salience with the Theory of Visual Attention.” <i>Advances in Cognitive
    Psychology</i> 12, no. 1 (2016): 20–38. <a href="https://doi.org/10.5709/acp-0184-1">https://doi.org/10.5709/acp-0184-1</a>.'
  ieee: 'A. Krüger, J. Tünnermann, and I. Scharlau, “Fast and conspicuous? Quantifying
    salience with the theory of visual attention.,” <i>Advances in Cognitive Psychology</i>,
    vol. 12, no. 1, pp. 20–38, 2016, doi: <a href="https://doi.org/10.5709/acp-0184-1">10.5709/acp-0184-1</a>.'
  mla: Krüger, Alexander, et al. “Fast and Conspicuous? Quantifying Salience with
    the Theory of Visual Attention.” <i>Advances in Cognitive Psychology</i>, vol.
    12, no. 1, 2016, pp. 20–38, doi:<a href="https://doi.org/10.5709/acp-0184-1">10.5709/acp-0184-1</a>.
  short: A. Krüger, J. Tünnermann, I. Scharlau, Advances in Cognitive Psychology 12
    (2016) 20–38.
date_created: 2018-12-10T07:04:15Z
date_updated: 2022-06-06T16:21:09Z
department:
- _id: '424'
doi: 10.5709/acp-0184-1
funded_apc: '1'
intvolume: '        12'
issue: '1'
keyword:
- salience
- visual attention
- Bayesian inference
- theory of visual attention
- computational modeling
- Visual Attention
- Computational Modeling
- Inference
- Judgment
- Statistical Probability
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: http://ac-psych.org/en/download-pdf/volume/12/issue/1/id/185
oa: '1'
page: 20 - 38
publication: Advances in Cognitive Psychology
publication_identifier:
  issn:
  - 1895-1171
publication_status: published
status: public
title: Fast and conspicuous? Quantifying salience with the theory of visual attention.
type: journal_article
user_id: '42165'
volume: 12
year: '2016'
...
---
_id: '11846'
abstract:
- lang: eng
  text: In this paper, we present a new technique for automatic speech recognition
    (ASR) in reverberant environments. Our approach is aimed at the enhancement of
    the logarithmic Mel power spectrum, which is computed at an intermediate stage
    to obtain the widely used Mel frequency cepstral coefficients (MFCCs). Given the
    reverberant logarithmic Mel power spectral coefficients (LMPSCs), a minimum mean
    square error estimate of the clean LMPSCs is computed by carrying out Bayesian
    inference. We employ switching linear dynamical models as an a priori model for
    the dynamics of the clean LMPSCs. Further, we derive a stochastic observation
    model which relates the clean to the reverberant LMPSCs through a simplified model
    of the room impulse response (RIR). This model requires only two parameters, namely
    RIR energy and reverberation time, which can be estimated from the captured microphone
    signal. The performance of the proposed enhancement technique is studied on the
    AURORA5 database and compared to that of constrained maximum-likelihood linear
    regression (CMLLR). It is shown by experimental results that our approach significantly
    outperforms CMLLR and that up to 80\% of the errors caused by the reverberation
    are recovered. In addition to the fact that the approach is compatible with the
    standard MFCC feature vectors, it leaves the ASR back-end unchanged. It is of
    moderate computational complexity and suitable for real time applications.
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. Model-Based Feature Enhancement for Reverberant Speech
    Recognition. <i>IEEE Transactions on Audio, Speech, and Language Processing</i>.
    2010;18(7):1692-1707. doi:<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>
  apa: Krueger, A., &#38; Haeb-Umbach, R. (2010). Model-Based Feature Enhancement
    for Reverberant Speech Recognition. <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, <i>18</i>(7), 1692–1707. <a href="https://doi.org/10.1109/TASL.2010.2049684">https://doi.org/10.1109/TASL.2010.2049684</a>
  bibtex: '@article{Krueger_Haeb-Umbach_2010, title={Model-Based Feature Enhancement
    for Reverberant Speech Recognition}, volume={18}, DOI={<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>},
    number={7}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
    author={Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2010}, pages={1692–1707}
    }'
  chicago: 'Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
    for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i> 18, no. 7 (2010): 1692–1707. <a href="https://doi.org/10.1109/TASL.2010.2049684">https://doi.org/10.1109/TASL.2010.2049684</a>.'
  ieee: A. Krueger and R. Haeb-Umbach, “Model-Based Feature Enhancement for Reverberant
    Speech Recognition,” <i>IEEE Transactions on Audio, Speech, and Language Processing</i>,
    vol. 18, no. 7, pp. 1692–1707, 2010.
  mla: Krueger, Alexander, and Reinhold Haeb-Umbach. “Model-Based Feature Enhancement
    for Reverberant Speech Recognition.” <i>IEEE Transactions on Audio, Speech, and
    Language Processing</i>, vol. 18, no. 7, 2010, pp. 1692–707, doi:<a href="https://doi.org/10.1109/TASL.2010.2049684">10.1109/TASL.2010.2049684</a>.
  short: A. Krueger, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
    Processing 18 (2010) 1692–1707.
date_created: 2019-07-12T05:29:23Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASL.2010.2049684
intvolume: '        18'
issue: '7'
keyword:
- ASR
- AURORA5 database
- automatic speech recognition
- Bayesian inference
- belief networks
- CMLLR
- computational complexity
- constrained maximum likelihood linear regression
- least mean squares methods
- LMPSC computation
- logarithmic Mel power spectrum
- maximum likelihood estimation
- Mel frequency cepstral coefficients
- MFCC feature vectors
- microphone signal
- minimum mean square error estimation
- model-based feature enhancement
- regression analysis
- reverberant speech recognition
- reverberation
- RIR energy
- room impulse response
- speech recognition
- stochastic observation model
- stochastic processes
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2010/KrHa10.pdf
oa: '1'
page: 1692-1707
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Model-Based Feature Enhancement for Reverberant Speech Recognition
type: journal_article
user_id: '44006'
volume: 18
year: '2010'
...
---
_id: '11939'
abstract:
- lang: eng
  text: In this paper a switching linear dynamical model (SLDM) approach for speech
    feature enhancement is improved by employing more accurate models for the dynamics
    of speech and noise. The model of the clean speech feature trajectory is improved
    by augmenting the state vector to capture information derived from the delta features.
    Further a hidden noise state variable is introduced to obtain a more elaborated
    model for the noise dynamics. Approximate Bayesian inference in the SLDM is carried
    out by a bank of extended Kalman filters, whose outputs are combined according
    to the a posteriori probability of the individual state models. Experimental results
    on the AURORA2 database show improved recognition accuracy.
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. Modeling the dynamics of speech and noise for speech
    feature enhancement in ASR. In: <i>IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2008)</i>. ; 2008:4409-4412. doi:<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>'
  apa: Windmann, S., &#38; Haeb-Umbach, R. (2008). Modeling the dynamics of speech
    and noise for speech feature enhancement in ASR. In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i> (pp. 4409–4412).
    <a href="https://doi.org/10.1109/ICASSP.2008.4518633">https://doi.org/10.1109/ICASSP.2008.4518633</a>
  bibtex: '@inproceedings{Windmann_Haeb-Umbach_2008, title={Modeling the dynamics
    of speech and noise for speech feature enhancement in ASR}, DOI={<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
    (ICASSP 2008)}, author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2008},
    pages={4409–4412} }'
  chicago: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
    and Noise for Speech Feature Enhancement in ASR.” In <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 4409–12, 2008. <a
    href="https://doi.org/10.1109/ICASSP.2008.4518633">https://doi.org/10.1109/ICASSP.2008.4518633</a>.
  ieee: S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise
    for speech feature enhancement in ASR,” in <i>IEEE International Conference on
    Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–4412.
  mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
    and Noise for Speech Feature Enhancement in ASR.” <i>IEEE International Conference
    on Acoustics, Speech and Signal Processing (ICASSP 2008)</i>, 2008, pp. 4409–12,
    doi:<a href="https://doi.org/10.1109/ICASSP.2008.4518633">10.1109/ICASSP.2008.4518633</a>.
  short: 'S. Windmann, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
    Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.'
date_created: 2019-07-12T05:31:11Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2008.4518633
keyword:
- a posteriori probability
- AURORA2 database
- Bayesian inference
- Bayes methods
- channel bank filters
- extended Kalman filter banks
- hidden noise state variable
- Kalman filters
- noise dynamics
- speech enhancement
- speech feature enhancement
- speech feature trajectory
- switching linear dynamical model approach
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://groups.uni-paderborn.de/nt/pubs/2008/WiHa08-1.pdf
oa: '1'
page: 4409-4412
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
  (ICASSP 2008)
status: public
title: Modeling the dynamics of speech and noise for speech feature enhancement in
  ASR
type: conference
user_id: '44006'
year: '2008'
...