---
_id: '29204'
abstract:
- lang: eng
text: 'An analysis of an optical Nyquist pulse synthesizer using Mach-Zehnder modulators
is presented. The analysis allows to predict the upper limit of the effective
number of bits of this type of photonic digital-to-analog converter. The analytical
solution has been verified by means of electro-optic simulations. With this analysis
the limiting factor for certain scenarios: relative intensity noise, distortions
by driving the Mach-Zehnder modulator, or the signal generator phase noise can
quickly be identified.'
author:
- first_name: Christian
full_name: Kress, Christian
id: '13256'
last_name: Kress
- first_name: Meysam
full_name: Bahmanian, Meysam
id: '69233'
last_name: Bahmanian
- first_name: Tobias
full_name: Schwabe, Tobias
id: '39217'
last_name: Schwabe
- first_name: J. Christoph
full_name: Scheytt, J. Christoph
id: '37144'
last_name: Scheytt
orcid: https://orcid.org/0000-0002-5950-6618
citation:
ama: Kress C, Bahmanian M, Schwabe T, Scheytt JC. Analysis of the effects of jitter,
relative intensity noise, and nonlinearity on a photonic digital-to-analog converter
based on optical Nyquist pulse synthesis. Opt Express. 2021;29(15):23671–23681.
doi:10.1364/OE.427424
apa: Kress, C., Bahmanian, M., Schwabe, T., & Scheytt, J. C. (2021). Analysis
of the effects of jitter, relative intensity noise, and nonlinearity on a photonic
digital-to-analog converter based on optical Nyquist pulse synthesis. Opt.
Express, 29(15), 23671–23681. https://doi.org/10.1364/OE.427424
bibtex: '@article{Kress_Bahmanian_Schwabe_Scheytt_2021, title={Analysis of the effects
of jitter, relative intensity noise, and nonlinearity on a photonic digital-to-analog
converter based on optical Nyquist pulse synthesis}, volume={29}, DOI={10.1364/OE.427424},
number={15}, journal={Opt. Express}, publisher={OSA}, author={Kress, Christian
and Bahmanian, Meysam and Schwabe, Tobias and Scheytt, J. Christoph}, year={2021},
pages={23671–23681} }'
chicago: 'Kress, Christian, Meysam Bahmanian, Tobias Schwabe, and J. Christoph Scheytt.
“Analysis of the Effects of Jitter, Relative Intensity Noise, and Nonlinearity
on a Photonic Digital-to-Analog Converter Based on Optical Nyquist Pulse Synthesis.”
Opt. Express 29, no. 15 (2021): 23671–23681. https://doi.org/10.1364/OE.427424.'
ieee: 'C. Kress, M. Bahmanian, T. Schwabe, and J. C. Scheytt, “Analysis of the effects
of jitter, relative intensity noise, and nonlinearity on a photonic digital-to-analog
converter based on optical Nyquist pulse synthesis,” Opt. Express, vol.
29, no. 15, pp. 23671–23681, 2021, doi: 10.1364/OE.427424.'
mla: Kress, Christian, et al. “Analysis of the Effects of Jitter, Relative Intensity
Noise, and Nonlinearity on a Photonic Digital-to-Analog Converter Based on Optical
Nyquist Pulse Synthesis.” Opt. Express, vol. 29, no. 15, OSA, 2021, pp.
23671–23681, doi:10.1364/OE.427424.
short: C. Kress, M. Bahmanian, T. Schwabe, J.C. Scheytt, Opt. Express 29 (2021)
23671–23681.
date_created: 2022-01-10T11:51:47Z
date_updated: 2023-06-16T06:56:27Z
department:
- _id: '58'
- _id: '230'
doi: 10.1364/OE.427424
intvolume: ' 29'
issue: '15'
keyword:
- Analog to digital converters
- Diode lasers
- Laser sources
- Phase noise
- Signal processing
- Wavelength division multiplexers
language:
- iso: eng
page: 23671–23681
project:
- _id: '302'
grant_number: '403154102'
name: 'PONyDAC: PONyDAC II - Präziser Optischer Nyquist-Puls-Synthesizer DAC'
- _id: '299'
grant_number: 13N14882
name: 'NyPhE: NyPhE - Nyquist Silicon Photonics Engine'
publication: Opt. Express
publisher: OSA
related_material:
link:
- relation: confirmation
url: https://pubmed.ncbi.nlm.nih.gov/34614628/
status: public
title: Analysis of the effects of jitter, relative intensity noise, and nonlinearity
on a photonic digital-to-analog converter based on optical Nyquist pulse synthesis
type: journal_article
user_id: '13256'
volume: 29
year: '2021'
...
---
_id: '11739'
abstract:
- lang: eng
text: Noise tracking is an important component of speech enhancement algorithms.
Of the many noise trackers proposed, Minimum Statistics (MS) is a particularly
popular one due to its simple parameterization and at the same time excellent
performance. In this paper we propose to further reduce the number of MS parameters
by giving an alternative derivation of an optimal smoothing constant. At the same
time the noise tracking performance is improved as is demonstrated by experiments
employing speech degraded by various noise types and at different SNR values.
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. On Optimal Smoothing in Minimum Statistics Based
Noise Tracking. In: Interspeech 2015. ; 2015:1785-1789.'
apa: Chinaev, A., & Haeb-Umbach, R. (2015). On Optimal Smoothing in Minimum
Statistics Based Noise Tracking. In Interspeech 2015 (pp. 1785–1789).
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2015, title={On Optimal Smoothing in
Minimum Statistics Based Noise Tracking}, booktitle={Interspeech 2015}, author={Chinaev,
Aleksej and Haeb-Umbach, Reinhold}, year={2015}, pages={1785–1789} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum
Statistics Based Noise Tracking.” In Interspeech 2015, 1785–89, 2015.
ieee: A. Chinaev and R. Haeb-Umbach, “On Optimal Smoothing in Minimum Statistics
Based Noise Tracking,” in Interspeech 2015, 2015, pp. 1785–1789.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “On Optimal Smoothing in Minimum
Statistics Based Noise Tracking.” Interspeech 2015, 2015, pp. 1785–89.
short: 'A. Chinaev, R. Haeb-Umbach, in: Interspeech 2015, 2015, pp. 1785–1789.'
date_created: 2019-07-12T05:27:19Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
keyword:
- speech enhancement
- noise tracking
- optimal smoothing
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/ChHa15.pdf
oa: '1'
page: 1785-1789
publication: Interspeech 2015
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2015/ChHa15_Poster.pdf
status: public
title: On Optimal Smoothing in Minimum Statistics Based Noise Tracking
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11813'
abstract:
- lang: eng
text: 'The parametric Bayesian Feature Enhancement (BFE) and a datadriven Denoising
Autoencoder (DA) both bring performance gains in severe single-channel speech
recognition conditions. The first can be adjusted to different conditions by an
appropriate parameter setting, while the latter needs to be trained on conditions
similar to the ones expected at decoding time, making it vulnerable to a mismatch
between training and test conditions. We use a DNN backend and study reverberant
ASR under three types of mismatch conditions: different room reverberation times,
different speaker to microphone distances and the difference between artificially
reverberated data and the recordings in a reverberant environment. We show that
for these mismatch conditions BFE can provide the targets for a DA. This unsupervised
adaptation provides a performance gain over the direct use of BFE and even enables
to compensate for the mismatch of real and simulated reverberant data.'
author:
- first_name: Jahn
full_name: Heymann, Jahn
id: '9168'
last_name: Heymann
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: P.
full_name: Golik, P.
last_name: Golik
- first_name: R.
full_name: Schlueter, R.
last_name: Schlueter
citation:
ama: 'Heymann J, Haeb-Umbach R, Golik P, Schlueter R. Unsupervised adaptation of
a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr under
mismatch conditions. In: Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference On. ; 2015:5053-5057. doi:10.1109/ICASSP.2015.7178933'
apa: Heymann, J., Haeb-Umbach, R., Golik, P., & Schlueter, R. (2015). Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions. In Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on (pp. 5053–5057). https://doi.org/10.1109/ICASSP.2015.7178933
bibtex: '@inproceedings{Heymann_Haeb-Umbach_Golik_Schlueter_2015, title={Unsupervised
adaptation of a denoising autoencoder by Bayesian Feature Enhancement for reverberant
asr under mismatch conditions}, DOI={10.1109/ICASSP.2015.7178933},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on}, author={Heymann, Jahn and Haeb-Umbach, Reinhold and Golik, P.
and Schlueter, R.}, year={2015}, pages={5053–5057} }'
chicago: Heymann, Jahn, Reinhold Haeb-Umbach, P. Golik, and R. Schlueter. “Unsupervised
Adaptation of a Denoising Autoencoder by Bayesian Feature Enhancement for Reverberant
Asr under Mismatch Conditions.” In Acoustics, Speech and Signal Processing
(ICASSP), 2015 IEEE International Conference On, 5053–57, 2015. https://doi.org/10.1109/ICASSP.2015.7178933.
ieee: J. Heymann, R. Haeb-Umbach, P. Golik, and R. Schlueter, “Unsupervised adaptation
of a denoising autoencoder by Bayesian Feature Enhancement for reverberant asr
under mismatch conditions,” in Acoustics, Speech and Signal Processing (ICASSP),
2015 IEEE International Conference on, 2015, pp. 5053–5057.
mla: Heymann, Jahn, et al. “Unsupervised Adaptation of a Denoising Autoencoder by
Bayesian Feature Enhancement for Reverberant Asr under Mismatch Conditions.” Acoustics,
Speech and Signal Processing (ICASSP), 2015 IEEE International Conference On,
2015, pp. 5053–57, doi:10.1109/ICASSP.2015.7178933.
short: 'J. Heymann, R. Haeb-Umbach, P. Golik, R. Schlueter, in: Acoustics, Speech
and Signal Processing (ICASSP), 2015 IEEE International Conference On, 2015, pp.
5053–5057.'
date_created: 2019-07-12T05:28:45Z
date_updated: 2022-01-06T06:51:09Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2015.7178933
keyword:
- codecs
- signal denoising
- speech recognition
- Bayesian feature enhancement
- denoising autoencoder
- reverberant ASR
- single-channel speech recognition
- speaker to microphone distances
- unsupervised adaptation
- Adaptation models
- Noise reduction
- Reverberation
- Speech
- Speech recognition
- Training
- deep neuronal networks
- denoising autoencoder
- feature enhancement
- robust speech recognition
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2015/hey_icassp_2015.pdf
oa: '1'
page: 5053-5057
publication: Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International
Conference on
status: public
title: Unsupervised adaptation of a denoising autoencoder by Bayesian Feature Enhancement
for reverberant asr under mismatch conditions
type: conference
user_id: '44006'
year: '2015'
...
---
_id: '11861'
abstract:
- lang: eng
text: 'In this contribution we present a theoretical and experimental investigation
into the effects of reverberation and noise on features in the logarithmic mel
power spectral domain, an intermediate stage in the computation of the mel frequency
cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining
insight into the complex interaction between clean speech, noise, and noisy reverberant
speech features is essential for any ASR system to be robust against noise and
reverberation present in distant microphone input signals. The findings are gathered
in a probabilistic formulation of an observation model which may be used in model-based
feature compensation schemes. The proposed observation model extends previous
models in three major directions: First, the contribution of additive background
noise to the observation error is explicitly taken into account. Second, an energy
compensation constant is introduced which ensures an unbiased estimate of the
reverberant speech features, and, third, a recursive variant of the observation
model is developed resulting in reduced computational complexity when used in
model-based feature compensation. The experimental section is used to evaluate
the accuracy of the model and to describe how its parameters can be determined
from test 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. A New Observation Model in the Logarithmic
Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.
IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2014;22(1):95-109.
doi:10.1109/TASLP.2013.2285480
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2014). A New Observation
Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition
of Noisy Reverberant Speech. IEEE/ACM Transactions on Audio, Speech, and Language
Processing, 22(1), 95–109. https://doi.org/10.1109/TASLP.2013.2285480
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2014, title={A New Observation Model
in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of
Noisy Reverberant Speech}, volume={22}, DOI={10.1109/TASLP.2013.2285480},
number={1}, journal={IEEE/ACM Transactions on Audio, Speech, and Language Processing},
author={Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold}, year={2014},
pages={95–109} }'
chicago: 'Leutnant, Volker, Alexander Krueger, and Reinhold Haeb-Umbach. “A New
Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic
Recognition of Noisy Reverberant Speech.” IEEE/ACM Transactions on Audio, Speech,
and Language Processing 22, no. 1 (2014): 95–109. https://doi.org/10.1109/TASLP.2013.2285480.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “A New Observation Model in the
Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant
Speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing,
vol. 22, no. 1, pp. 95–109, 2014.
mla: Leutnant, Volker, et al. “A New Observation Model in the Logarithmic Mel Power
Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech.” IEEE/ACM
Transactions on Audio, Speech, and Language Processing, vol. 22, no. 1, 2014,
pp. 95–109, doi:10.1109/TASLP.2013.2285480.
short: V. Leutnant, A. Krueger, R. Haeb-Umbach, IEEE/ACM Transactions on Audio,
Speech, and Language Processing 22 (2014) 95–109.
date_created: 2019-07-12T05:29:41Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2013.2285480
intvolume: ' 22'
issue: '1'
keyword:
- computational complexity
- reverberation
- speech recognition
- automatic speech recognition
- background noise
- clean speech
- computational complexity
- energy compensation
- logarithmic mel power spectral domain
- mel frequency cepstral coefficients
- microphone input signals
- model-based feature compensation schemes
- noisy reverberant speech automatic recognition
- noisy reverberant speech features
- reverberation
- Atmospheric modeling
- Computational modeling
- Noise
- Noise measurement
- Reverberation
- Speech
- Vectors
- Model-based feature compensation
- observation model for reverberant and noisy speech
- recursive observation model
- robust automatic speech recognition
language:
- iso: eng
page: 95-109
publication: IEEE/ACM Transactions on Audio, Speech, and Language Processing
publication_identifier:
issn:
- 2329-9290
status: public
title: A New Observation Model in the Logarithmic Mel Power Spectral Domain for the
Automatic Recognition of Noisy Reverberant Speech
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11867'
abstract:
- lang: eng
text: 'New waves of consumer-centric applications, such as voice search and voice
interaction with mobile devices and home entertainment systems, increasingly require
automatic speech recognition (ASR) to be robust to the full range of real-world
noise and other acoustic distorting conditions. Despite its practical importance,
however, the inherent links between and distinctions among the myriad of methods
for noise-robust ASR have yet to be carefully studied in order to advance the
field further. To this end, it is critical to establish a solid, consistent, and
common mathematical foundation for noise-robust ASR, which is lacking at present.
This article is intended to fill this gap and to provide a thorough overview of
modern noise-robust techniques for ASR developed over the past 30 years. We emphasize
methods that are proven to be successful and that are likely to sustain or expand
their future applicability. We distill key insights from our comprehensive overview
in this field and take a fresh look at a few old problems, which nevertheless
are still highly relevant today. Specifically, we have analyzed and categorized
a wide range of noise-robust techniques using five different criteria: 1) feature-domain
vs. model-domain processing, 2) the use of prior knowledge about the acoustic
environment distortion, 3) the use of explicit environment-distortion models,
4) deterministic vs. uncertainty processing, and 5) the use of acoustic models
trained jointly with the same feature enhancement or model adaptation process
used in the testing stage. With this taxonomy-oriented review, we equip the reader
with the insight to choose among techniques and with the awareness of the performance-complexity
tradeoffs. The pros and cons of using different noise-robust ASR techniques in
practical application scenarios are provided as a guide to interested practitioners.
The current challenges and future research directions in this field is also carefully
analyzed.'
author:
- first_name: Jinyu
full_name: Li, Jinyu
last_name: Li
- first_name: Li
full_name: Deng, Li
last_name: Deng
- first_name: Yifan
full_name: Gong, Yifan
last_name: Gong
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: Li J, Deng L, Gong Y, Haeb-Umbach R. An Overview of Noise-Robust Automatic
Speech Recognition. IEEE Transactions on Audio, Speech and Language Processing.
2014;22(4):745-777. doi:10.1109/TASLP.2014.2304637
apa: Li, J., Deng, L., Gong, Y., & Haeb-Umbach, R. (2014). An Overview of Noise-Robust
Automatic Speech Recognition. IEEE Transactions on Audio, Speech and Language
Processing, 22(4), 745–777. https://doi.org/10.1109/TASLP.2014.2304637
bibtex: '@article{Li_Deng_Gong_Haeb-Umbach_2014, title={An Overview of Noise-Robust
Automatic Speech Recognition}, volume={22}, DOI={10.1109/TASLP.2014.2304637},
number={4}, journal={IEEE Transactions on Audio, Speech and Language Processing},
author={Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold}, year={2014},
pages={745–777} }'
chicago: 'Li, Jinyu, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach. “An Overview
of Noise-Robust Automatic Speech Recognition.” IEEE Transactions on Audio,
Speech and Language Processing 22, no. 4 (2014): 745–77. https://doi.org/10.1109/TASLP.2014.2304637.'
ieee: J. Li, L. Deng, Y. Gong, and R. Haeb-Umbach, “An Overview of Noise-Robust
Automatic Speech Recognition,” IEEE Transactions on Audio, Speech and Language
Processing, vol. 22, no. 4, pp. 745–777, 2014.
mla: Li, Jinyu, et al. “An Overview of Noise-Robust Automatic Speech Recognition.”
IEEE Transactions on Audio, Speech and Language Processing, vol. 22, no.
4, 2014, pp. 745–77, doi:10.1109/TASLP.2014.2304637.
short: J. Li, L. Deng, Y. Gong, R. Haeb-Umbach, IEEE Transactions on Audio, Speech
and Language Processing 22 (2014) 745–777.
date_created: 2019-07-12T05:29:47Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/TASLP.2014.2304637
intvolume: ' 22'
issue: '4'
keyword:
- Speech recognition
- compensation
- distortion modeling
- joint model training
- noise
- robustness
- uncertainty processing
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6732927
oa: '1'
page: 745-777
publication: IEEE Transactions on Audio, Speech and Language Processing
status: public
title: An Overview of Noise-Robust Automatic Speech Recognition
type: journal_article
user_id: '44006'
volume: 22
year: '2014'
...
---
_id: '11716'
abstract:
- lang: eng
text: The accuracy of automatic speech recognition systems in noisy and reverberant
environments can be improved notably by exploiting the uncertainty of the estimated
speech features using so-called uncertainty-of-observation techniques. In this
paper, we introduce a new Bayesian decision rule that can serve as a mathematical
framework from which both known and new uncertainty-of-observation techniques
can be either derived or approximated. The new decision rule in its direct form
leads to the new significance decoding approach for Gaussian mixture models, which
results in better performance compared to standard uncertainty-of-observation
techniques in different additive and convolutive noise scenarios.
author:
- first_name: Ahmed H.
full_name: Abdelaziz, Ahmed H.
last_name: Abdelaziz
- first_name: Steffen
full_name: Zeiler, Steffen
last_name: Zeiler
- first_name: Dorothea
full_name: Kolossa, Dorothea
last_name: Kolossa
- first_name: Volker
full_name: Leutnant, Volker
last_name: Leutnant
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Abdelaziz AH, Zeiler S, Kolossa D, Leutnant V, Haeb-Umbach R. GMM-based significance
decoding. In: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference On. ; 2013:6827-6831. doi:10.1109/ICASSP.2013.6638984'
apa: Abdelaziz, A. H., Zeiler, S., Kolossa, D., Leutnant, V., & Haeb-Umbach,
R. (2013). GMM-based significance decoding. In Acoustics, Speech and Signal
Processing (ICASSP), 2013 IEEE International Conference on (pp. 6827–6831).
https://doi.org/10.1109/ICASSP.2013.6638984
bibtex: '@inproceedings{Abdelaziz_Zeiler_Kolossa_Leutnant_Haeb-Umbach_2013, title={GMM-based
significance decoding}, DOI={10.1109/ICASSP.2013.6638984},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on}, author={Abdelaziz, Ahmed H. and Zeiler, Steffen and Kolossa, Dorothea
and Leutnant, Volker and Haeb-Umbach, Reinhold}, year={2013}, pages={6827–6831}
}'
chicago: Abdelaziz, Ahmed H., Steffen Zeiler, Dorothea Kolossa, Volker Leutnant,
and Reinhold Haeb-Umbach. “GMM-Based Significance Decoding.” In Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
6827–31, 2013. https://doi.org/10.1109/ICASSP.2013.6638984.
ieee: A. H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, and R. Haeb-Umbach, “GMM-based
significance decoding,” in Acoustics, Speech and Signal Processing (ICASSP),
2013 IEEE International Conference on, 2013, pp. 6827–6831.
mla: Abdelaziz, Ahmed H., et al. “GMM-Based Significance Decoding.” Acoustics,
Speech and Signal Processing (ICASSP), 2013 IEEE International Conference On,
2013, pp. 6827–31, doi:10.1109/ICASSP.2013.6638984.
short: 'A.H. Abdelaziz, S. Zeiler, D. Kolossa, V. Leutnant, R. Haeb-Umbach, in:
Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference
On, 2013, pp. 6827–6831.'
date_created: 2019-07-12T05:26:53Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638984
keyword:
- Bayes methods
- Gaussian processes
- convolution
- decision theory
- decoding
- noise
- reverberation
- speech coding
- speech recognition
- Bayesian decision rule
- GMM
- Gaussian mixture models
- additive noise scenarios
- automatic speech recognition systems
- convolutive noise scenarios
- decoding approach
- mathematical framework
- reverberant environments
- significance decoding
- speech feature estimation
- uncertainty-of-observation techniques
- Hidden Markov models
- Maximum likelihood decoding
- Noise
- Speech
- Speech recognition
- Uncertainty
- Uncertainty-of-observation
- modified imputation
- noise robust speech recognition
- significance decoding
- uncertainty decoding
language:
- iso: eng
page: 6827-6831
publication: Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International
Conference on
publication_identifier:
issn:
- 1520-6149
status: public
title: GMM-based significance decoding
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '11740'
abstract:
- lang: eng
text: In this contribution we derive the Maximum A-Posteriori (MAP) estimates of
the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations.
We assume the distortion to be white Gaussian noise of known mean and variance.
An approximate conjugate prior of the GMM parameters is derived allowing for a
computationally efficient implementation in a sequential estimation framework.
Simulations on artificially generated data demonstrate the superiority of the
proposed method compared to the Maximum Likelihood technique and to the ordinary
MAP approach, whose estimates are corrected by the known statistics of the distortion
in a straightforward manner.
author:
- first_name: Aleksej
full_name: Chinaev, Aleksej
last_name: Chinaev
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Chinaev A, Haeb-Umbach R. MAP-based Estimation of the Parameters of a Gaussian
Mixture Model in the Presence of Noisy Observations. In: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:3352-3356.
doi:10.1109/ICASSP.2013.6638279'
apa: Chinaev, A., & Haeb-Umbach, R. (2013). MAP-based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations. In 38th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013)
(pp. 3352–3356). https://doi.org/10.1109/ICASSP.2013.6638279
bibtex: '@inproceedings{Chinaev_Haeb-Umbach_2013, title={MAP-based Estimation of
the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations},
DOI={10.1109/ICASSP.2013.6638279},
booktitle={38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)}, author={Chinaev, Aleksej and Haeb-Umbach, Reinhold}, year={2013},
pages={3352–3356} }'
chicago: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the
Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations.”
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 3352–56, 2013. https://doi.org/10.1109/ICASSP.2013.6638279.
ieee: A. Chinaev and R. Haeb-Umbach, “MAP-based Estimation of the Parameters of
a Gaussian Mixture Model in the Presence of Noisy Observations,” in 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–3356.
mla: Chinaev, Aleksej, and Reinhold Haeb-Umbach. “MAP-Based Estimation of the Parameters
of a Gaussian Mixture Model in the Presence of Noisy Observations.” 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), 2013,
pp. 3352–56, doi:10.1109/ICASSP.2013.6638279.
short: 'A. Chinaev, R. Haeb-Umbach, in: 38th International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2013), 2013, pp. 3352–3356.'
date_created: 2019-07-12T05:27:20Z
date_updated: 2022-01-06T06:51:08Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2013.6638279
keyword:
- Gaussian noise
- maximum likelihood estimation
- parameter estimation
- GMM parameter
- Gaussian mixture model
- MAP estimation
- Map-based estimation
- maximum a-posteriori estimation
- maximum likelihood technique
- noisy observation
- sequential estimation framework
- white Gaussian noise
- Additive noise
- Gaussian mixture model
- Maximum likelihood estimation
- Noise measurement
- Gaussian mixture model
- Maximum a posteriori estimation
- Maximum likelihood estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13.pdf
oa: '1'
page: 3352-3356
publication: 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013)
publication_identifier:
issn:
- 1520-6149
related_material:
link:
- description: Poster
relation: supplementary_material
url: https://groups.uni-paderborn.de/nt/pubs/2013/ChHa13_Poster.pdf
status: public
title: MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence
of Noisy Observations
type: conference
user_id: '44006'
year: '2013'
...
---
_id: '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. IEEE Transactions on Audio, Speech, and
Language Processing. 2013;21(8):1640-1652. doi:10.1109/TASL.2013.2258013
apa: Leutnant, V., Krueger, A., & Haeb-Umbach, R. (2013). Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition. IEEE Transactions on
Audio, Speech, and Language Processing, 21(8), 1640–1652. https://doi.org/10.1109/TASL.2013.2258013
bibtex: '@article{Leutnant_Krueger_Haeb-Umbach_2013, title={Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition}, volume={21}, DOI={10.1109/TASL.2013.2258013},
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.” IEEE
Transactions on Audio, Speech, and Language Processing 21, no. 8 (2013): 1640–52.
https://doi.org/10.1109/TASL.2013.2258013.'
ieee: V. Leutnant, A. Krueger, and R. Haeb-Umbach, “Bayesian Feature Enhancement
for Reverberation and Noise Robust Speech Recognition,” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 21, no. 8, pp. 1640–1652, 2013.
mla: Leutnant, Volker, et al. “Bayesian Feature Enhancement for Reverberation and
Noise Robust Speech Recognition.” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 21, no. 8, 2013, pp. 1640–52, doi:10.1109/TASL.2013.2258013.
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: '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: 38th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2013). ; 2013:863-867.
doi:10.1109/ICASSP.2013.6637771'
apa: Vu, D. H. T., & Haeb-Umbach, R. (2013). Using the turbo principle for exploiting
temporal and spectral correlations in speech presence probability estimation.
In 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013) (pp. 863–867). https://doi.org/10.1109/ICASSP.2013.6637771
bibtex: '@inproceedings{Vu_Haeb-Umbach_2013, title={Using the turbo principle for
exploiting temporal and spectral correlations in speech presence probability estimation},
DOI={10.1109/ICASSP.2013.6637771},
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 38th International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2013), 863–67, 2013. https://doi.org/10.1109/ICASSP.2013.6637771.
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 38th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2013), 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.”
38th International Conference on Acoustics, Speech and Signal Processing (ICASSP
2013), 2013, pp. 863–67, doi:10.1109/ICASSP.2013.6637771.
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: '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: 37th International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2012). ; 2012.'
apa: Chinaev, A., Krueger, A., Tran Vu, D. H., & Haeb-Umbach, R. (2012). Improved
Noise Power Spectral Density Tracking by a MAP-based Postprocessor. In 37th
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2012).
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 37th International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2012), 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 37th International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2012), 2012.
mla: Chinaev, Aleksej, et al. “Improved Noise Power Spectral Density Tracking by
a MAP-Based Postprocessor.” 37th International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2012), 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: '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. IEEE Transactions
on Audio, Speech, and Language Processing. 2011;19(1):206-219. doi:10.1109/TASL.2010.2047324
apa: Krueger, A., Warsitz, E., & Haeb-Umbach, R. (2011). Speech Enhancement
With a GSC-Like Structure Employing Eigenvector-Based Transfer Function Ratios
Estimation. IEEE Transactions on Audio, Speech, and Language Processing,
19(1), 206–219. https://doi.org/10.1109/TASL.2010.2047324
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={10.1109/TASL.2010.2047324},
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.” IEEE Transactions on Audio, Speech, and Language Processing
19, no. 1 (2011): 206–19. https://doi.org/10.1109/TASL.2010.2047324.'
ieee: A. Krueger, E. Warsitz, and R. Haeb-Umbach, “Speech Enhancement With a GSC-Like
Structure Employing Eigenvector-Based Transfer Function Ratios Estimation,” IEEE
Transactions on Audio, Speech, and Language Processing, 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.” IEEE Transactions on
Audio, Speech, and Language Processing, vol. 19, no. 1, 2011, pp. 206–19,
doi:10.1109/TASL.2010.2047324.
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: '11913'
abstract:
- lang: eng
text: In this paper we propose to employ directional statistics in a complex vector
space to approach the problem of blind speech separation in the presence of spatially
correlated noise. We interpret the values of the short time Fourier transform
of the microphone signals to be draws from a mixture of complex Watson distributions,
a probabilistic model which naturally accounts for spatial aliasing. The parameters
of the density are related to the a priori source probabilities, the power of
the sources and the transfer function ratios from sources to sensors. Estimation
formulas are derived for these parameters by employing the Expectation Maximization
(EM) algorithm. The E-step corresponds to the estimation of the source presence
probabilities for each time-frequency bin, while the M-step leads to a maximum
signal-to-noise ratio (MaxSNR) beamformer in the presence of uncertainty about
the source activity. Experimental results are reported for an implementation in
a generalized sidelobe canceller (GSC) like spatial beamforming configuration
for 3 speech sources with significant coherent noise in reverberant environments,
demonstrating the usefulness of the novel modeling framework.
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 employing directional statistics
in an Expectation Maximization framework. In: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2010). ; 2010:241-244.
doi:10.1109/ICASSP.2010.5495994'
apa: Tran Vu, D. H., & Haeb-Umbach, R. (2010). Blind speech separation employing
directional statistics in an Expectation Maximization framework. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010) (pp. 241–244).
https://doi.org/10.1109/ICASSP.2010.5495994
bibtex: '@inproceedings{Tran Vu_Haeb-Umbach_2010, title={Blind speech separation
employing directional statistics in an Expectation Maximization framework}, DOI={10.1109/ICASSP.2010.5495994},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2010)}, author={Tran Vu, Dang Hai and Haeb-Umbach, Reinhold}, year={2010},
pages={241–244} }'
chicago: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
Directional Statistics in an Expectation Maximization Framework.” In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 241–44,
2010. https://doi.org/10.1109/ICASSP.2010.5495994.
ieee: D. H. Tran Vu and R. Haeb-Umbach, “Blind speech separation employing directional
statistics in an Expectation Maximization framework,” in IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010,
pp. 241–244.
mla: Tran Vu, Dang Hai, and Reinhold Haeb-Umbach. “Blind Speech Separation Employing
Directional Statistics in an Expectation Maximization Framework.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2010), 2010,
pp. 241–44, doi:10.1109/ICASSP.2010.5495994.
short: 'D.H. Tran Vu, R. Haeb-Umbach, in: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2010), 2010, pp. 241–244.'
date_created: 2019-07-12T05:30:40Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2010.5495994
keyword:
- array signal processing
- blind source separation
- blind speech separation
- complex vector space
- complex Watson distribution
- directional statistics
- expectation-maximisation algorithm
- expectation maximization algorithm
- Fourier transform
- Fourier transforms
- generalized sidelobe canceller
- interference suppression
- maximum signal-to-noise ratio beamformer
- microphone signal
- probabilistic model
- spatial aliasing
- spatial beamforming configuration
- speech enhancement
- statistical distributions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2010/DaHa10-2.pdf
oa: '1'
page: 241-244
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2010)
status: public
title: Blind speech separation employing directional statistics in an Expectation
Maximization framework
type: conference
user_id: '44006'
year: '2010'
...
---
_id: '11723'
abstract:
- lang: eng
text: In this paper we present a novel vehicle tracking algorithm, which is based
on multi-level sensor fusion of GPS (global positioning system) with Inertial
Measurement Unit sensor data. It is shown that the robustness of the system to
temporary dropouts of the GPS signal, which may occur due to limited visibility
of satellites in narrow street canyons or tunnels, is greatly improved by sensor
fusion. We further demonstrate how the observation and state noise covariances
of the employed Kalman filters can be estimated alongside the filtering by an
application of the Expectation-Maximization algorithm. The proposed time-variant
multi-level Kalman filter is shown to outperform an Interacting Multiple Model
approach while at the same time being computationally less demanding.
author:
- first_name: Maik
full_name: Bevermeier, Maik
last_name: Bevermeier
- first_name: Sven
full_name: Peschke, Sven
last_name: Peschke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Bevermeier M, Peschke S, Haeb-Umbach R. Robust vehicle localization based
on multi-level sensor fusion and online parameter estimation. In: 6th Workshop
on Positioning Navigation and Communication (WPNC 2009). ; 2009:235-242. doi:10.1109/WPNC.2009.4907833'
apa: Bevermeier, M., Peschke, S., & Haeb-Umbach, R. (2009). Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation. In 6th
Workshop on Positioning Navigation and Communication (WPNC 2009) (pp. 235–242).
https://doi.org/10.1109/WPNC.2009.4907833
bibtex: '@inproceedings{Bevermeier_Peschke_Haeb-Umbach_2009, title={Robust vehicle
localization based on multi-level sensor fusion and online parameter estimation},
DOI={10.1109/WPNC.2009.4907833},
booktitle={6th Workshop on Positioning Navigation and Communication (WPNC 2009)},
author={Bevermeier, Maik and Peschke, Sven and Haeb-Umbach, Reinhold}, year={2009},
pages={235–242} }'
chicago: Bevermeier, Maik, Sven Peschke, and Reinhold Haeb-Umbach. “Robust Vehicle
Localization Based on Multi-Level Sensor Fusion and Online Parameter Estimation.”
In 6th Workshop on Positioning Navigation and Communication (WPNC 2009),
235–42, 2009. https://doi.org/10.1109/WPNC.2009.4907833.
ieee: M. Bevermeier, S. Peschke, and R. Haeb-Umbach, “Robust vehicle localization
based on multi-level sensor fusion and online parameter estimation,” in 6th
Workshop on Positioning Navigation and Communication (WPNC 2009), 2009, pp.
235–242.
mla: Bevermeier, Maik, et al. “Robust Vehicle Localization Based on Multi-Level
Sensor Fusion and Online Parameter Estimation.” 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–42, doi:10.1109/WPNC.2009.4907833.
short: 'M. Bevermeier, S. Peschke, R. Haeb-Umbach, in: 6th Workshop on Positioning
Navigation and Communication (WPNC 2009), 2009, pp. 235–242.'
date_created: 2019-07-12T05:27:01Z
date_updated: 2022-01-06T06:51:07Z
department:
- _id: '54'
doi: 10.1109/WPNC.2009.4907833
keyword:
- covariance matrices
- expectation-maximisation algorithm
- expectation-maximization algorithm
- global positioning system
- Global Positioning System
- GPS
- inertial measurement unit
- interacting multiple model approach
- Kalman filters
- multilevel sensor fusion
- narrow street canyons
- narrow tunnels
- online parameter estimation
- parameter estimation
- road vehicles
- robust vehicle localization
- sensor fusion
- state noise covariances
- time-variant multilevel Kalman filter
- vehicle tracking algorithm
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/BePeHa09.pdf
oa: '1'
page: 235-242
publication: 6th Workshop on Positioning Navigation and Communication (WPNC 2009)
status: public
title: Robust vehicle localization based on multi-level sensor fusion and online parameter
estimation
type: conference
user_id: '44006'
year: '2009'
...
---
_id: '11938'
abstract:
- lang: eng
text: In this paper, parameter estimation of a state-space model of noise or noisy
speech cepstra is investigated. A blockwise EM algorithm is derived for the estimation
of the state and observation noise covariance from noise-only input data. It is
supposed to be used during the offline training mode of a speech recognizer. Further
a sequential online EM algorithm is developed to adapt the observation noise covariance
on noisy speech cepstra at its input. The estimated parameters are then used in
model-based speech feature enhancement for noise-robust automatic speech recognition.
Experiments on the AURORA4 database lead to improved recognition results with
a linear state model compared to the assumption of stationary noise.
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. Parameter Estimation of a State-Space Model of Noise
for Robust Speech Recognition. IEEE Transactions on Audio, Speech, and Language
Processing. 2009;17(8):1577-1590. doi:10.1109/TASL.2009.2023172
apa: Windmann, S., & Haeb-Umbach, R. (2009). Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition. IEEE Transactions on Audio, Speech,
and Language Processing, 17(8), 1577–1590. https://doi.org/10.1109/TASL.2009.2023172
bibtex: '@article{Windmann_Haeb-Umbach_2009, title={Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition}, volume={17}, DOI={10.1109/TASL.2009.2023172},
number={8}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Windmann, Stefan and Haeb-Umbach, Reinhold}, year={2009}, pages={1577–1590}
}'
chicago: 'Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a
State-Space Model of Noise for Robust Speech Recognition.” IEEE Transactions
on Audio, Speech, and Language Processing 17, no. 8 (2009): 1577–90. https://doi.org/10.1109/TASL.2009.2023172.'
ieee: S. Windmann and R. Haeb-Umbach, “Parameter Estimation of a State-Space Model
of Noise for Robust Speech Recognition,” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 17, no. 8, pp. 1577–1590, 2009.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Parameter Estimation of a State-Space
Model of Noise for Robust Speech Recognition.” IEEE Transactions on Audio,
Speech, and Language Processing, vol. 17, no. 8, 2009, pp. 1577–90, doi:10.1109/TASL.2009.2023172.
short: S. Windmann, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 17 (2009) 1577–1590.
date_created: 2019-07-12T05:31:09Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2009.2023172
intvolume: ' 17'
issue: '8'
keyword:
- AURORA4 database
- blockwise EM algorithm
- covariance analysis
- linear state model
- noise covariance
- noise-robust automatic speech recognition
- noisy speech cepstra
- offline training mode
- parameter estimation
- speech recognition
- speech recognition equipment
- speech recognizer
- state-space methods
- state-space model
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2009/WiHa09-2.pdf
oa: '1'
page: 1577-1590
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Parameter Estimation of a State-Space Model of Noise for Robust Speech Recognition
type: journal_article
user_id: '44006'
volume: 17
year: '2009'
...
---
_id: '11935'
abstract:
- lang: eng
text: The generalized sidelobe canceller by Griffith and Jim is a robust beamforming
method to enhance a desired (speech) signal in the presence of stationary noise.
Its performance depends to a high degree on the construction of the blocking matrix
which produces noise reference signals for the subsequent adaptive interference
canceller. Especially in reverberated environments the beamformer may suffer from
signal leakage and reduced noise suppression. In this paper a new blocking matrix
is proposed. It is based on a generalized eigenvalue problem whose solution provides
an indirect estimation of the transfer functions from the source to the sensors.
The quality of the new generalized eigenvector blocking matrix is studied in simulated
rooms with different reverberation times and is compared to alternatives proposed
in the literature.
author:
- first_name: Ernst
full_name: Warsitz, Ernst
last_name: Warsitz
- 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: 'Warsitz E, Krueger A, Haeb-Umbach R. Speech enhancement with a new generalized
eigenvector blocking matrix for application in a generalized sidelobe canceller.
In: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2008). ; 2008:73-76. doi:10.1109/ICASSP.2008.4517549'
apa: Warsitz, E., Krueger, A., & Haeb-Umbach, R. (2008). Speech enhancement
with a new generalized eigenvector blocking matrix for application in a generalized
sidelobe canceller. In IEEE International Conference on Acoustics, Speech and
Signal Processing (ICASSP 2008) (pp. 73–76). https://doi.org/10.1109/ICASSP.2008.4517549
bibtex: '@inproceedings{Warsitz_Krueger_Haeb-Umbach_2008, title={Speech enhancement
with a new generalized eigenvector blocking matrix for application in a generalized
sidelobe canceller}, DOI={10.1109/ICASSP.2008.4517549},
booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2008)}, author={Warsitz, Ernst and Krueger, Alexander and Haeb-Umbach,
Reinhold}, year={2008}, pages={73–76} }'
chicago: Warsitz, Ernst, Alexander Krueger, and Reinhold Haeb-Umbach. “Speech Enhancement
with a New Generalized Eigenvector Blocking Matrix for Application in a Generalized
Sidelobe Canceller.” In IEEE International Conference on Acoustics, Speech
and Signal Processing (ICASSP 2008), 73–76, 2008. https://doi.org/10.1109/ICASSP.2008.4517549.
ieee: E. Warsitz, A. Krueger, and R. Haeb-Umbach, “Speech enhancement with a new
generalized eigenvector blocking matrix for application in a generalized sidelobe
canceller,” in IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP 2008), 2008, pp. 73–76.
mla: Warsitz, Ernst, et al. “Speech Enhancement with a New Generalized Eigenvector
Blocking Matrix for Application in a Generalized Sidelobe Canceller.” IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2008),
2008, pp. 73–76, doi:10.1109/ICASSP.2008.4517549.
short: 'E. Warsitz, A. Krueger, R. Haeb-Umbach, in: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 73–76.'
date_created: 2019-07-12T05:31:06Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/ICASSP.2008.4517549
keyword:
- adaptive interference canceller
- adaptive signal processing
- array signal processing
- beamforming method
- eigenvalues and eigenfunctions
- generalized eigenvector blocking matrix
- generalized sidelobe canceller
- interference suppression
- matrix algebra
- noise suppression
- speech enhancement
- transfer function estimation
- transfer functions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2008/WaKrHa08.pdf
oa: '1'
page: 73-76
publication: IEEE International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2008)
status: public
title: Speech enhancement with a new generalized eigenvector blocking matrix for application
in a generalized sidelobe canceller
type: conference
user_id: '44006'
year: '2008'
...
---
_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: IEEE International Conference on Acoustics,
Speech and Signal Processing (ICASSP 2008). ; 2008:4409-4412. doi:10.1109/ICASSP.2008.4518633'
apa: Windmann, S., & Haeb-Umbach, R. (2008). Modeling the dynamics of speech
and noise for speech feature enhancement in ASR. In IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008) (pp. 4409–4412).
https://doi.org/10.1109/ICASSP.2008.4518633
bibtex: '@inproceedings{Windmann_Haeb-Umbach_2008, title={Modeling the dynamics
of speech and noise for speech feature enhancement in ASR}, DOI={10.1109/ICASSP.2008.4518633},
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 IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008), 4409–12, 2008. https://doi.org/10.1109/ICASSP.2008.4518633.
ieee: S. Windmann and R. Haeb-Umbach, “Modeling the dynamics of speech and noise
for speech feature enhancement in ASR,” in IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–4412.
mla: Windmann, Stefan, and Reinhold Haeb-Umbach. “Modeling the Dynamics of Speech
and Noise for Speech Feature Enhancement in ASR.” IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2008), 2008, pp. 4409–12,
doi:10.1109/ICASSP.2008.4518633.
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'
...
---
_id: '11883'
abstract:
- lang: eng
text: In this paper, we experimentally evaluate algorithms for velocity estimation
of a GSM 900 mobile terminal which are based on the analysis of the statistical
properties of the fast fading process. It is shown how theses statistics can be
obtained from the training sequences present in downlink transmission bursts without
establishing an active connection. Realistic simulations of a GSM channel according
to the COST 207 channel models have been conducted. These models incorporate effects
like multipath propagation, fading, cochannel interference and additive noise.
It is shown that velocity estimation by searching for the maximum slope of the
power density spectrum of the fast fading performs best.
author:
- first_name: Sven
full_name: Peschke, Sven
last_name: Peschke
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Peschke S, Haeb-Umbach R. Velocity Estimation of Mobile Terminals by Exploiting
GSM Downlink Signalling. In: 4th Workshop on Positioning Navigation and Communication
(WPNC 2007). ; 2007:217-222. doi:10.1109/WPNC.2007.353637'
apa: Peschke, S., & Haeb-Umbach, R. (2007). Velocity Estimation of Mobile Terminals
by Exploiting GSM Downlink Signalling. In 4th Workshop on Positioning Navigation
and Communication (WPNC 2007) (pp. 217–222). https://doi.org/10.1109/WPNC.2007.353637
bibtex: '@inproceedings{Peschke_Haeb-Umbach_2007, title={Velocity Estimation of
Mobile Terminals by Exploiting GSM Downlink Signalling}, DOI={10.1109/WPNC.2007.353637},
booktitle={4th Workshop on Positioning Navigation and Communication (WPNC 2007)},
author={Peschke, Sven and Haeb-Umbach, Reinhold}, year={2007}, pages={217–222}
}'
chicago: Peschke, Sven, and Reinhold Haeb-Umbach. “Velocity Estimation of Mobile
Terminals by Exploiting GSM Downlink Signalling.” In 4th Workshop on Positioning
Navigation and Communication (WPNC 2007), 217–22, 2007. https://doi.org/10.1109/WPNC.2007.353637.
ieee: S. Peschke and R. Haeb-Umbach, “Velocity Estimation of Mobile Terminals by
Exploiting GSM Downlink Signalling,” in 4th Workshop on Positioning Navigation
and Communication (WPNC 2007), 2007, pp. 217–222.
mla: Peschke, Sven, and Reinhold Haeb-Umbach. “Velocity Estimation of Mobile Terminals
by Exploiting GSM Downlink Signalling.” 4th Workshop on Positioning Navigation
and Communication (WPNC 2007), 2007, pp. 217–22, doi:10.1109/WPNC.2007.353637.
short: 'S. Peschke, R. Haeb-Umbach, in: 4th Workshop on Positioning Navigation and
Communication (WPNC 2007), 2007, pp. 217–222.'
date_created: 2019-07-12T05:30:06Z
date_updated: 2022-01-06T06:51:11Z
department:
- _id: '54'
doi: 10.1109/WPNC.2007.353637
keyword:
- additive noise
- cellular radio
- channel estimation
- cochannel interference
- COST 207 channel models
- downlink transmission bursts
- fading channels
- fading process
- GSM downlink signalling
- mobile terminals
- multipath channels
- multipath propagation
- power density spectrum
- statistical analysis
- statistical properties
- telecommunication links
- telecommunication terminals
- velocity estimation
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/PeHa07.pdf
oa: '1'
page: 217-222
publication: 4th Workshop on Positioning Navigation and Communication (WPNC 2007)
status: public
title: Velocity Estimation of Mobile Terminals by Exploiting GSM Downlink Signalling
type: conference
user_id: '44006'
year: '2007'
...
---
_id: '11927'
abstract:
- lang: eng
text: Maximizing the output signal-to-noise ratio (SNR) of a sensor array in the
presence of spatially colored noise leads to a generalized eigenvalue problem.
While this approach has extensively been employed in narrowband (antenna) array
beamforming, it is typically not used for broadband (microphone) array beamforming
due to the uncontrolled amount of speech distortion introduced by a narrowband
SNR criterion. In this paper, we show how the distortion of the desired signal
can be controlled by a single-channel post-filter, resulting in a performance
comparable to the generalized minimum variance distortionless response beamformer,
where arbitrary transfer functions relate the source and the microphones. Results
are given both for directional and diffuse noise. A novel gradient ascent adaptation
algorithm is presented, and its good convergence properties are experimentally
revealed by comparison with alternatives from the literature. A key feature of
the proposed beamformer is that it operates blindly, i.e., it neither requires
knowledge about the array geometry nor an explicit estimation of the transfer
functions from source to sensors or the direction-of-arrival.
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. Blind Acoustic Beamforming Based on Generalized Eigenvalue
Decomposition. IEEE Transactions on Audio, Speech, and Language Processing.
2007;15(5):1529-1539. doi:10.1109/TASL.2007.898454
apa: Warsitz, E., & Haeb-Umbach, R. (2007). Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition. IEEE Transactions on Audio, Speech,
and Language Processing, 15(5), 1529–1539. https://doi.org/10.1109/TASL.2007.898454
bibtex: '@article{Warsitz_Haeb-Umbach_2007, title={Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition}, volume={15}, DOI={10.1109/TASL.2007.898454},
number={5}, journal={IEEE Transactions on Audio, Speech, and Language Processing},
author={Warsitz, Ernst and Haeb-Umbach, Reinhold}, year={2007}, pages={1529–1539}
}'
chicago: 'Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming
Based on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio,
Speech, and Language Processing 15, no. 5 (2007): 1529–39. https://doi.org/10.1109/TASL.2007.898454.'
ieee: E. Warsitz and R. Haeb-Umbach, “Blind Acoustic Beamforming Based on Generalized
Eigenvalue Decomposition,” IEEE Transactions on Audio, Speech, and Language
Processing, vol. 15, no. 5, pp. 1529–1539, 2007.
mla: Warsitz, Ernst, and Reinhold Haeb-Umbach. “Blind Acoustic Beamforming Based
on Generalized Eigenvalue Decomposition.” IEEE Transactions on Audio, Speech,
and Language Processing, vol. 15, no. 5, 2007, pp. 1529–39, doi:10.1109/TASL.2007.898454.
short: E. Warsitz, R. Haeb-Umbach, IEEE Transactions on Audio, Speech, and Language
Processing 15 (2007) 1529–1539.
date_created: 2019-07-12T05:30:57Z
date_updated: 2022-01-06T06:51:12Z
department:
- _id: '54'
doi: 10.1109/TASL.2007.898454
intvolume: ' 15'
issue: '5'
keyword:
- acoustic signal processing
- arbitrary transfer function
- array signal processing
- blind acoustic beamforming
- direction-of-arrival
- direction-of-arrival estimation
- eigenvalues and eigenfunctions
- generalized eigenvalue decomposition
- gradient ascent adaptation algorithm
- microphone arrays
- microphones
- narrowband array beamforming
- sensor array
- single-channel post-filter
- spatially colored noise
- transfer functions
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://groups.uni-paderborn.de/nt/pubs/2007/WaHa07.pdf
oa: '1'
page: 1529-1539
publication: IEEE Transactions on Audio, Speech, and Language Processing
status: public
title: Blind Acoustic Beamforming Based on Generalized Eigenvalue Decomposition
type: journal_article
user_id: '44006'
volume: 15
year: '2007'
...
---
_id: '9535'
author:
- first_name: Alexander
full_name: Genzo, Alexander
last_name: Genzo
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Genzo A, Sextro W. Dynamic Behaviour of Elastic Bodies Coupled by Extended
Friction Contacts. In: Proceedings of ISMA - International Conference on Noise
and Vibration Engineering. ; 2006:51-52.'
apa: Genzo, A., & Sextro, W. (2006). Dynamic Behaviour of Elastic Bodies Coupled
by Extended Friction Contacts. In Proceedings of ISMA - International conference
on noise and vibration engineering (pp. 51–52).
bibtex: '@inproceedings{Genzo_Sextro_2006, title={Dynamic Behaviour of Elastic Bodies
Coupled by Extended Friction Contacts}, booktitle={Proceedings of ISMA - International
conference on noise and vibration engineering}, author={Genzo, Alexander and Sextro,
Walter}, year={2006}, pages={51–52} }'
chicago: Genzo, Alexander, and Walter Sextro. “Dynamic Behaviour of Elastic Bodies
Coupled by Extended Friction Contacts.” In Proceedings of ISMA - International
Conference on Noise and Vibration Engineering, 51–52, 2006.
ieee: A. Genzo and W. Sextro, “Dynamic Behaviour of Elastic Bodies Coupled by Extended
Friction Contacts,” in Proceedings of ISMA - International conference on noise
and vibration engineering, 2006, pp. 51–52.
mla: Genzo, Alexander, and Walter Sextro. “Dynamic Behaviour of Elastic Bodies Coupled
by Extended Friction Contacts.” Proceedings of ISMA - International Conference
on Noise and Vibration Engineering, 2006, pp. 51–52.
short: 'A. Genzo, W. Sextro, in: Proceedings of ISMA - International Conference
on Noise and Vibration Engineering, 2006, pp. 51–52.'
date_created: 2019-04-29T08:54:27Z
date_updated: 2022-01-06T07:04:16Z
department:
- _id: '151'
keyword:
- Noise
- Vibration engineering
language:
- iso: eng
page: 51-52
publication: Proceedings of ISMA - International conference on noise and vibration
engineering
status: public
title: Dynamic Behaviour of Elastic Bodies Coupled by Extended Friction Contacts
type: conference
user_id: '55222'
year: '2006'
...
---
_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: IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006). Vol 1. ; 2006:I.
doi:10.1109/ICASSP.2006.1660058'
apa: Windmann, S., & Haeb-Umbach, R. (2006). Iterative Speech Enhancement using
a Non-Linear Dynamic State Model of Speech and its Parameters. In IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006) (Vol.
1, p. I). https://doi.org/10.1109/ICASSP.2006.1660058
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={10.1109/ICASSP.2006.1660058},
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 IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP 2006),
1:I, 2006. https://doi.org/10.1109/ICASSP.2006.1660058.
ieee: S. Windmann and R. Haeb-Umbach, “Iterative Speech Enhancement using a Non-Linear
Dynamic State Model of Speech and its Parameters,” in IEEE International Conference
on Acoustics, Speech and Signal Processing (ICASSP 2006), 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.” IEEE International
Conference on Acoustics, Speech and Signal Processing (ICASSP 2006), vol.
1, 2006, p. I, doi:10.1109/ICASSP.2006.1660058.
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'
...