---
_id: '20868'
abstract:
- lang: eng
  text: 'This study proposes a simple theoretical framework that allows for assessing
    financial distress up to five years in advance. We jointly model financial distress
    by using two of its key driving factors: declining cash-generating ability and
    insufficient liquidity reserves. The model is based on stochastic processes and
    incorporates firm-level and industry-sector developments. A large-scale empirical
    implementation for US-listed firms over the period of 1980-2010 shows important
    improvements in the discriminatory accuracy and demonstrates incremental information
    content beyond state-of-the-art accounting and market-based prediction models.
    Consequently, this study might provide important ex ante warning signals for investors,
    regulators and practitioners.'
author:
- first_name: Sönke
  full_name: Sievers, Sönke
  id: '46447'
  last_name: Sievers
- first_name: Jan
  full_name: Klobucnik, Jan
  last_name: Klobucnik
- first_name: David
  full_name: Miersch, David
  last_name: Miersch
citation:
  ama: 'Sievers S, Klobucnik J, Miersch D. <i>Predicting Early Warning Signals of
    Financial Distress: Theory and Empirical Evidence</i>.; 2017. doi:<a href="https://doi.org/10.2139/ssrn.2237757">10.2139/ssrn.2237757</a>'
  apa: 'Sievers, S., Klobucnik, J., &#38; Miersch, D. (2017). <i>Predicting Early
    Warning Signals of Financial Distress: Theory and Empirical Evidence</i>. <a href="https://doi.org/10.2139/ssrn.2237757">https://doi.org/10.2139/ssrn.2237757</a>'
  bibtex: '@book{Sievers_Klobucnik_Miersch_2017, title={Predicting Early Warning Signals
    of Financial Distress: Theory and Empirical Evidence}, DOI={<a href="https://doi.org/10.2139/ssrn.2237757">10.2139/ssrn.2237757</a>},
    author={Sievers, Sönke and Klobucnik, Jan and Miersch, David}, year={2017} }'
  chicago: 'Sievers, Sönke, Jan Klobucnik, and David Miersch. <i>Predicting Early
    Warning Signals of Financial Distress: Theory and Empirical Evidence</i>, 2017.
    <a href="https://doi.org/10.2139/ssrn.2237757">https://doi.org/10.2139/ssrn.2237757</a>.'
  ieee: 'S. Sievers, J. Klobucnik, and D. Miersch, <i>Predicting Early Warning Signals
    of Financial Distress: Theory and Empirical Evidence</i>. 2017.'
  mla: 'Sievers, Sönke, et al. <i>Predicting Early Warning Signals of Financial Distress:
    Theory and Empirical Evidence</i>. 2017, doi:<a href="https://doi.org/10.2139/ssrn.2237757">10.2139/ssrn.2237757</a>.'
  short: 'S. Sievers, J. Klobucnik, D. Miersch, Predicting Early Warning Signals of
    Financial Distress: Theory and Empirical Evidence, 2017.'
date_created: 2021-01-05T11:44:45Z
date_updated: 2022-01-06T06:54:41Z
department:
- _id: '275'
doi: 10.2139/ssrn.2237757
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
main_file_link:
- url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2237757
page: '84'
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
  Evidence'
type: working_paper
user_id: '46447'
year: '2017'
...
---
_id: '5199'
abstract:
- lang: eng
  text: 'This study proposes a simple theoretical framework that allows for assessing
    financial distress up to five years in advance. We jointly model financial distress
    by using two of its key driving factors: declining cash-generating ability and
    insufficient liquidity reserves. The model is based on stochastic processes and
    incorporates firm-level and industry-sector developments. A large-scale empirical
    implementation for US-listed firms over the period of 1980-2010 shows important
    improvements in the discriminatory accuracy and demonstrates incremental information
    content beyond state-of-the-art accounting and market-based prediction models.
    Consequently, this study might provide important ex ante warning signals for investors,
    regulators and practitioners. '
author:
- first_name: Jan
  full_name: Klobucnik, Jan
  last_name: Klobucnik
- first_name: David
  full_name: Miersch, David
  last_name: Miersch
- first_name: Sönke
  full_name: Sievers, Sönke
  last_name: Sievers
citation:
  ama: 'Klobucnik J, Miersch D, Sievers S. Predicting Early Warning Signals of Financial
    Distress: Theory and Empirical Evidence. <i>SSRN Electronic Journal</i>. 2017.'
  apa: 'Klobucnik, J., Miersch, D., &#38; Sievers, S. (2017). Predicting Early Warning
    Signals of Financial Distress: Theory and Empirical Evidence. <i>SSRN Electronic
    Journal</i>.'
  bibtex: '@article{Klobucnik_Miersch_Sievers_2017, title={Predicting Early Warning
    Signals of Financial Distress: Theory and Empirical Evidence}, journal={SSRN Electronic
    Journal}, author={Klobucnik, Jan and Miersch, David and Sievers, Sönke}, year={2017}
    }'
  chicago: 'Klobucnik, Jan, David Miersch, and Sönke Sievers. “Predicting Early Warning
    Signals of Financial Distress: Theory and Empirical Evidence.” <i>SSRN Electronic
    Journal</i>, 2017.'
  ieee: 'J. Klobucnik, D. Miersch, and S. Sievers, “Predicting Early Warning Signals
    of Financial Distress: Theory and Empirical Evidence,” <i>SSRN Electronic Journal</i>,
    2017.'
  mla: 'Klobucnik, Jan, et al. “Predicting Early Warning Signals of Financial Distress:
    Theory and Empirical Evidence.” <i>SSRN Electronic Journal</i>, 2017.'
  short: J. Klobucnik, D. Miersch, S. Sievers, SSRN Electronic Journal (2017).
date_created: 2018-10-31T12:19:42Z
date_updated: 2022-01-06T07:01:43Z
department:
- _id: '275'
jel:
- C63
- C52
- C53
- G33
- M41
keyword:
- Financial distress prediction
- probability of default
- accounting information
- stochastic processes
- simulation
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
status: public
title: 'Predicting Early Warning Signals of Financial Distress: Theory and Empirical
  Evidence'
type: journal_article
user_id: '64756'
year: '2017'
...
---
_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: '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'
...